Package evaluation to test Turing on Julia 1.14.0-DEV.1688 (ee54f91d68*) started at 2026-02-10T08:59:02.647 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 11.25s ################################################################################ # Installation # Installing Turing... Resolving package versions... Updating `~/.julia/environments/v1.14/Project.toml` [fce5fe82] + Turing v0.42.8 Updating `~/.julia/environments/v1.14/Manifest.toml` [47edcb42] + ADTypes v1.21.0 [621f4979] + AbstractFFTs v1.5.0 [80f14c24] + AbstractMCMC v5.14.0 ⌅ [7a57a42e] + AbstractPPL v0.13.6 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.43 [79e6a3ab] + Adapt v4.4.0 [0bf59076] + AdvancedHMC v0.8.3 [5b7e9947] + AdvancedMH v0.8.9 [576499cb] + AdvancedPS v0.7.2 [b5ca4192] + AdvancedVI v0.6.2 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [4fba245c] + ArrayInterface v7.22.0 [13072b0f] + AxisAlgorithms v1.1.0 [39de3d68] + AxisArrays v0.4.8 [198e06fe] + BangBang v0.4.7 [9718e550] + Baselet v0.1.1 [76274a88] + Bijectors v0.15.16 [082447d4] + ChainRules v1.72.6 [d360d2e6] + ChainRulesCore v1.26.0 [0ca39b1e] + Chairmarks v1.3.1 [9e997f8a] + ChangesOfVariables v0.1.10 [861a8166] + Combinatorics v1.1.0 [38540f10] + CommonSolve v0.2.6 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [88cd18e8] + ConsoleProgressMonitor v0.1.2 [187b0558] + ConstructionBase v1.6.0 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.3 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [b429d917] + DensityInterface v0.4.0 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [a0c0ee7d] + DifferentiationInterface v0.7.16 [31c24e10] + Distributions v0.25.123 [ced4e74d] + DistributionsAD v0.6.58 [ffbed154] + DocStringExtensions v0.9.5 [366bfd00] + DynamicPPL v0.39.13 [cad2338a] + EllipticalSliceSampling v2.0.0 [4e289a0a] + EnumX v1.0.6 [e2ba6199] + ExprTools v0.1.10 [55351af7] + ExproniconLite v0.10.14 [b86e33f2] + FFTA v0.3.1 [9aa1b823] + FastClosures v0.3.2 [1a297f60] + FillArrays v1.16.0 [6a86dc24] + FiniteDiff v2.29.0 [f6369f11] + ForwardDiff v1.3.2 [069b7b12] + FunctionWrappers v1.1.3 [77dc65aa] + FunctionWrappersWrappers v0.1.3 [d9f16b24] + Functors v0.5.2 [46192b85] + GPUArraysCore v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [22cec73e] + InitialValues v0.3.1 [18e54dd8] + IntegerMathUtils v0.1.3 [a98d9a8b] + Interpolations v0.16.2 [8197267c] + IntervalSets v0.7.13 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.1 [682c06a0] + JSON v1.4.0 [ae98c720] + Jieko v0.2.1 [5ab0869b] + KernelDensity v0.6.11 [b964fa9f] + LaTeXStrings v1.4.0 [1d6d02ad] + LeftChildRightSiblingTrees v0.2.1 [6f1fad26] + Libtask v0.9.10 [d3d80556] + LineSearches v7.6.0 [6fdf6af0] + LogDensityProblems v2.2.0 [996a588d] + LogDensityProblemsAD v1.13.1 [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.2.0 [c7f686f2] + MCMCChains v7.7.0 [be115224] + MCMCDiagnosticTools v0.3.16 [e80e1ace] + MLJModelInterface v1.12.1 [1914dd2f] + MacroTools v0.5.16 [dbb5928d] + MappedArrays v0.4.3 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [dbe65cb8] + MistyClosures v2.1.0 [2e0e35c7] + Moshi v0.3.7 [46d2c3a1] + MuladdMacro v0.2.4 [d41bc354] + NLSolversBase v8.0.0 [77ba4419] + NaNMath v1.1.3 [86f7a689] + NamedArrays v0.10.5 [c020b1a1] + NaturalSort v1.0.0 [6fe1bfb0] + OffsetArrays v1.17.0 [429524aa] + Optim v2.0.1 [3bd65402] + Optimisers v0.4.7 [7f7a1694] + Optimization v5.4.0 [bca83a33] + OptimizationBase v4.2.0 [36348300] + OptimizationOptimJL v0.4.9 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.37 [69de0a69] + Parsers v2.8.3 [85a6dd25] + PositiveFactorizations v0.2.4 [d236fae5] + PreallocationTools v1.1.2 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.1 [08abe8d2] + PrettyTables v3.1.2 [27ebfcd6] + Primes v0.5.7 [33c8b6b6] + ProgressLogging v0.1.6 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [74087812] + Random123 v1.7.1 [e6cf234a] + RandomNumbers v1.6.0 [b3c3ace0] + RangeArrays v0.3.2 [c84ed2f1] + Ratios v0.4.5 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 [731186ca] + RecursiveArrayTools v3.47.0 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.9.0 [f2b01f46] + Roots v2.2.10 [7e49a35a] + RuntimeGeneratedFunctions v0.5.17 [26aad666] + SSMProblems v0.6.1 [0bca4576] + SciMLBase v2.138.0 [a6db7da4] + SciMLLogging v1.8.0 [c0aeaf25] + SciMLOperators v1.15.1 [431bcebd] + SciMLPublic v1.0.1 [53ae85a6] + SciMLStructures v1.10.0 [30f210dd] + ScientificTypesBase v3.1.0 [efcf1570] + Setfield v1.1.2 [a2af1166] + SortingAlgorithms v1.2.2 [9f842d2f] + SparseConnectivityTracer v1.2.0 [dc90abb0] + SparseInverseSubset v0.1.2 [0a514795] + SparseMatrixColorings v0.4.23 [276daf66] + SpecialFunctions v2.6.1 [171d559e] + SplittablesBase v0.1.15 [90137ffa] + StaticArrays v1.9.16 [1e83bf80] + StaticArraysCore v1.4.4 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.8.0 [2913bbd2] + StatsBase v0.34.10 [4c63d2b9] + StatsFuns v1.5.2 [892a3eda] + StringManipulation v0.4.2 [09ab397b] + StructArrays v0.7.2 [ec057cc2] + StructUtils v2.6.2 [2efcf032] + SymbolicIndexingInterface v0.3.46 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [5d786b92] + TerminalLoggers v0.1.7 [28d57a85] + Transducers v0.4.85 [fce5fe82] + Turing v0.42.8 [efce3f68] + WoodburyMatrices v1.1.0 [700de1a5] + ZygoteRules v0.2.7 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.13.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.13.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [de0858da] + Printf v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.0.0 [9e88b42a] + Serialization v1.11.0 [1a1011a3] + SharedArrays v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.13.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [4536629a] + OpenBLAS_jll v0.3.30+0 [05823500] + OpenLibm_jll v0.8.7+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [8e850b90] + libblastrampoline_jll v5.15.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 5.78s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ TestEnv ~/.julia/packages/TestEnv/h9a3r/src/julia-1.13/activate_set.jl:78 Precompiling package dependencies... Precompiling packages... 2449.9 ms ✓ DifferentiationInterface → DifferentiationInterfaceSparseConnectivityTracerExt 1064.1 ms ✓ DifferentiationInterface → DifferentiationInterfaceFiniteDiffExt 1459.7 ms ✓ DifferentiationInterface → DifferentiationInterfaceStaticArraysExt 1847.3 ms ✓ DifferentiationInterface → DifferentiationInterfaceSparseMatrixColoringsExt ERROR: LoadError: UndefVarError: `WorldView` not defined in `Compiler` Suggestion: check for spelling errors or missing imports. Stacktrace:  [1] getproperty(x::Module, f::Symbol)  @ Base ./Base_compiler.jl:50  [2] top-level scope  @ ~/.julia/packages/Mooncake/qc0nh/src/interpreter/abstract_interpretation.jl:89  [3] include(mapexpr::Function, mod::Module, _path::String)  @ Base ./Base.jl:310  [4] top-level scope  @ ~/.julia/packages/Mooncake/qc0nh/src/Mooncake.jl:124  [5] include(mod::Module, _path::String)  @ Base ./Base.jl:309  [6] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3309  [7] top-level scope  @ stdin:5  [8] eval(m::Module, e::Any)  @ Core ./boot.jl:489  [9] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3151  [10] include_string  @ ./loading.jl:3161 [inlined]  [11] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:342  [12] _start()  @ Base ./client.jl:585 in expression starting at /home/pkgeval/.julia/packages/Mooncake/qc0nh/src/interpreter/abstract_interpretation.jl:89 in expression starting at /home/pkgeval/.julia/packages/Mooncake/qc0nh/src/Mooncake.jl:1 in expression starting at stdin:5 ✗ Mooncake 2286.0 ms ✓ DifferentiationInterface → DifferentiationInterfaceFiniteDifferencesExt 3307.5 ms ✓ SciMLBase → SciMLBaseDifferentiationInterfaceExt 1473.0 ms ✓ DifferentiationInterface → DifferentiationInterfaceForwardDiffExt 3943.9 ms ✓ AbstractPPL 6333.0 ms ✓ AdvancedVI 14126.5 ms ✓ Bijectors → BijectorsReverseDiffExt 9229.1 ms ✓ DifferentiationInterface → DifferentiationInterfaceReverseDiffExt 1446.2 ms ✓ NLSolversBase ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace:  [1] error(s::String)  @ Base ./error.jl:44  [2] __require_prelocked(pkg::Base.PkgId, env::String)  @ Base ./loading.jl:2861  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)  @ Base ./loading.jl:2715  [4] macro expansion  @ ./loading.jl:2642 [inlined]  [5] macro expansion  @ ./lock.jl:376 [inlined]  [6] __require(into::Module, mod::Symbol)  @ Base ./loading.jl:2606  [7] require  @ ./loading.jl:2582 [inlined]  [8] eval_import_path  @ ./module.jl:36 [inlined]  [9] eval_import_path_all(at::Module, path::Expr, keyword::String)  @ Base ./module.jl:60  [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr})  @ Base ./module.jl:101  [11] top-level scope  @ ~/.julia/packages/Mooncake/qc0nh/ext/MooncakeLogExpFunctionsExt.jl:6  [12] include(mod::Module, _path::String)  @ Base ./Base.jl:309  [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3309  [14] top-level scope  @ stdin:5  [15] eval(m::Module, e::Any)  @ Core ./boot.jl:489  [16] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3151  [17] include_string  @ ./loading.jl:3161 [inlined]  [18] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:342  [19] _start()  @ Base ./client.jl:585 in expression starting at /home/pkgeval/.julia/packages/Mooncake/qc0nh/ext/MooncakeLogExpFunctionsExt.jl:1 in expression starting at stdin:5 ✗ Mooncake → MooncakeLogExpFunctionsExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace:  [1] error(s::String)  @ Base ./error.jl:44  [2] __require_prelocked(pkg::Base.PkgId, env::String)  @ Base ./loading.jl:2861  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)  @ Base ./loading.jl:2715  [4] macro expansion  @ ./loading.jl:2642 [inlined]  [5] macro expansion  @ ./lock.jl:376 [inlined]  [6] __require(into::Module, mod::Symbol)  @ Base ./loading.jl:2606  [7] require  @ ./loading.jl:2582 [inlined]  [8] eval_import_path  @ ./module.jl:36 [inlined]  [9] eval_import_path_all(at::Module, path::Expr, keyword::String)  @ Base ./module.jl:60  [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr})  @ Base ./module.jl:101  [11] top-level scope  @ ~/.julia/packages/Mooncake/qc0nh/ext/MooncakeFunctionWrappersExt.jl:5  [12] include(mod::Module, _path::String)  @ Base ./Base.jl:309  [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3309  [14] top-level scope  @ stdin:5  [15] eval(m::Module, e::Any)  @ Core ./boot.jl:489  [16] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3151  [17] include_string  @ ./loading.jl:3161 [inlined]  [18] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:342  [19] _start()  @ Base ./client.jl:585 in expression starting at /home/pkgeval/.julia/packages/Mooncake/qc0nh/ext/MooncakeFunctionWrappersExt.jl:1 in expression starting at stdin:5 ✗ Mooncake → MooncakeFunctionWrappersExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace:  [1] error(s::String)  @ Base ./error.jl:44  [2] __require_prelocked(pkg::Base.PkgId, env::String)  @ Base ./loading.jl:2861  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)  @ Base ./loading.jl:2715  [4] macro expansion  @ ./loading.jl:2642 [inlined]  [5] macro expansion  @ ./lock.jl:376 [inlined]  [6] __require(into::Module, mod::Symbol)  @ Base ./loading.jl:2606  [7] require  @ ./loading.jl:2582 [inlined]  [8] eval_import_path  @ ./module.jl:36 [inlined]  [9] eval_import_path_all(at::Module, path::Expr, keyword::String)  @ Base ./module.jl:60  [10] _eval_using  @ ./module.jl:137 [inlined]  [11] _eval_using(to::Module, path::Expr)  @ Base ./module.jl:137  [12] top-level scope  @ ~/.julia/packages/Mooncake/qc0nh/ext/MooncakeSpecialFunctionsExt.jl:3  [13] include(mod::Module, _path::String)  @ Base ./Base.jl:309  [14] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3309  [15] top-level scope  @ stdin:5  [16] eval(m::Module, e::Any)  @ Core ./boot.jl:489  [17] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3151  [18] include_string  @ ./loading.jl:3161 [inlined]  [19] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:342  [20] _start()  @ Base ./client.jl:585 in expression starting at /home/pkgeval/.julia/packages/Mooncake/qc0nh/ext/MooncakeSpecialFunctionsExt.jl:1 in expression starting at stdin:5 ✗ Mooncake → MooncakeSpecialFunctionsExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace:  [1] error(s::String)  @ Base ./error.jl:44  [2] __require_prelocked(pkg::Base.PkgId, env::String)  @ Base ./loading.jl:2861  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)  @ Base ./loading.jl:2715  [4] macro expansion  @ ./loading.jl:2642 [inlined]  [5] macro expansion  @ ./lock.jl:376 [inlined]  [6] __require(into::Module, mod::Symbol)  @ Base ./loading.jl:2606  [7] require  @ ./loading.jl:2582 [inlined]  [8] eval_import_path  @ ./module.jl:36 [inlined]  [9] eval_import_path_all(at::Module, path::Expr, keyword::String)  @ Base ./module.jl:60  [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr})  @ Base ./module.jl:101  [11] top-level scope  @ ~/.julia/packages/DifferentiationInterface/M8gIf/ext/DifferentiationInterfaceMooncakeExt/DifferentiationInterfaceMooncakeExt.jl:5  [12] include(mod::Module, _path::String)  @ Base ./Base.jl:309  [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3309  [14] top-level scope  @ stdin:5  [15] eval(m::Module, e::Any)  @ Core ./boot.jl:489  [16] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3151  [17] include_string  @ ./loading.jl:3161 [inlined]  [18] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:342  [19] _start()  @ Base ./client.jl:585 in expression starting at /home/pkgeval/.julia/packages/DifferentiationInterface/M8gIf/ext/DifferentiationInterfaceMooncakeExt/DifferentiationInterfaceMooncakeExt.jl:1 in expression starting at stdin:5 ✗ DifferentiationInterface → DifferentiationInterfaceMooncakeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace:  [1] error(s::String)  @ Base ./error.jl:44  [2] __require_prelocked(pkg::Base.PkgId, env::String)  @ Base ./loading.jl:2861  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)  @ Base ./loading.jl:2715  [4] macro expansion  @ ./loading.jl:2642 [inlined]  [5] macro expansion  @ ./lock.jl:376 [inlined]  [6] __require(into::Module, mod::Symbol)  @ Base ./loading.jl:2606  [7] require  @ ./loading.jl:2582 [inlined]  [8] eval_import_path  @ ./module.jl:36 [inlined]  [9] eval_import_path_all(at::Module, path::Expr, keyword::String)  @ Base ./module.jl:60  [10] _eval_using  @ ./module.jl:137 [inlined]  [11] _eval_using(to::Module, path::Expr)  @ Base ./module.jl:137  [12] top-level scope  @ ~/.julia/packages/SciMLBase/emsSz/ext/SciMLBaseMooncakeExt.jl:3  [13] include(mod::Module, _path::String)  @ Base ./Base.jl:309  [14] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3309  [15] top-level scope  @ stdin:5  [16] eval(m::Module, e::Any)  @ Core ./boot.jl:489  [17] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3151  [18] include_string  @ ./loading.jl:3161 [inlined]  [19] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:342  [20] _start()  @ Base ./client.jl:585 in expression starting at /home/pkgeval/.julia/packages/SciMLBase/emsSz/ext/SciMLBaseMooncakeExt.jl:1 in expression starting at stdin:5 ✗ SciMLBase → SciMLBaseMooncakeExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace:  [1] error(s::String)  @ Base ./error.jl:44  [2] __require_prelocked(pkg::Base.PkgId, env::String)  @ Base ./loading.jl:2861  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)  @ Base ./loading.jl:2715  [4] macro expansion  @ ./loading.jl:2642 [inlined]  [5] macro expansion  @ ./lock.jl:376 [inlined]  [6] __require(into::Module, mod::Symbol)  @ Base ./loading.jl:2606  [7] require  @ ./loading.jl:2582 [inlined]  [8] eval_import_path  @ ./module.jl:36 [inlined]  [9] eval_import_path_all(at::Module, path::Expr, keyword::String)  @ Base ./module.jl:60  [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr})  @ Base ./module.jl:101  [11] top-level scope  @ ~/.julia/packages/Bijectors/L8omP/ext/BijectorsMooncakeExt.jl:3  [12] include(mod::Module, _path::String)  @ Base ./Base.jl:309  [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3309  [14] top-level scope  @ stdin:5  [15] eval(m::Module, e::Any)  @ Core ./boot.jl:489  [16] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3151  [17] include_string  @ ./loading.jl:3161 [inlined]  [18] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:342  [19] _start()  @ Base ./client.jl:585 in expression starting at /home/pkgeval/.julia/packages/Bijectors/L8omP/ext/BijectorsMooncakeExt.jl:1 in expression starting at stdin:5 ✗ Bijectors → BijectorsMooncakeExt 7124.5 ms ✓ OptimizationBase 5503.4 ms ✓ AbstractPPL → AbstractPPLDistributionsExt ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace:  [1] error(s::String)  @ Base ./error.jl:44  [2] __require_prelocked(pkg::Base.PkgId, env::String)  @ Base ./loading.jl:2861  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)  @ Base ./loading.jl:2715  [4] macro expansion  @ ./loading.jl:2642 [inlined]  [5] macro expansion  @ ./lock.jl:376 [inlined]  [6] __require(into::Module, mod::Symbol)  @ Base ./loading.jl:2606  [7] require  @ ./loading.jl:2582 [inlined]  [8] eval_import_path  @ ./module.jl:36 [inlined]  [9] eval_import_path_all(at::Module, path::Expr, keyword::String)  @ Base ./module.jl:60  [10] _eval_using  @ ./module.jl:137 [inlined]  [11] _eval_using(to::Module, path::Expr)  @ Base ./module.jl:137  [12] top-level scope  @ ~/.julia/packages/AdvancedVI/2WrpF/ext/AdvancedVIMooncakeExt.jl:5  [13] include(mod::Module, _path::String)  @ Base ./Base.jl:309  [14] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3309  [15] top-level scope  @ stdin:5  [16] eval(m::Module, e::Any)  @ Core ./boot.jl:489  [17] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3151  [18] include_string  @ ./loading.jl:3161 [inlined]  [19] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:342  [20] _start()  @ Base ./client.jl:585 in expression starting at /home/pkgeval/.julia/packages/AdvancedVI/2WrpF/ext/AdvancedVIMooncakeExt.jl:1 in expression starting at stdin:5 ✗ AdvancedVI → AdvancedVIMooncakeExt 12778.6 ms ✓ AdvancedVI → AdvancedVIReverseDiffExt 3605.2 ms ✓ AdvancedVI → AdvancedVIBijectorsExt 13169.2 ms ✓ Bijectors → BijectorsReverseDiffChainRulesExt 1573.8 ms ✓ LineSearches 9066.5 ms ✓ OptimizationBase → OptimizationReverseDiffExt 884.7 ms ✓ OptimizationBase → OptimizationFiniteDiffExt 1307.8 ms ✓ OptimizationBase → OptimizationForwardDiffExt 4447.2 ms ✓ Optimization 4594.6 ms ✓ OptimizationNLopt 6013.4 ms ✓ OptimizationBBO 19317.4 ms ✓ DynamicPPL 5039.0 ms ✓ Optim ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"), "Mooncake") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=1, inline=true, opt_level=0) Stacktrace:  [1] error(s::String)  @ Base ./error.jl:44  [2] __require_prelocked(pkg::Base.PkgId, env::String)  @ Base ./loading.jl:2861  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)  @ Base ./loading.jl:2715  [4] macro expansion  @ ./loading.jl:2642 [inlined]  [5] macro expansion  @ ./lock.jl:376 [inlined]  [6] __require(into::Module, mod::Symbol)  @ Base ./loading.jl:2606  [7] require  @ ./loading.jl:2582 [inlined]  [8] eval_import_path  @ ./module.jl:36 [inlined]  [9] eval_import_path_all(at::Module, path::Expr, keyword::String)  @ Base ./module.jl:60  [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr})  @ Base ./module.jl:101  [11] top-level scope  @ ~/.julia/packages/DynamicPPL/GG1GP/ext/DynamicPPLMooncakeExt.jl:4  [12] include(mod::Module, _path::String)  @ Base ./Base.jl:309  [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3309  [14] top-level scope  @ stdin:5  [15] eval(m::Module, e::Any)  @ Core ./boot.jl:489  [16] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3151  [17] include_string  @ ./loading.jl:3161 [inlined]  [18] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:342  [19] _start()  @ Base ./client.jl:585 in expression starting at /home/pkgeval/.julia/packages/DynamicPPL/GG1GP/ext/DynamicPPLMooncakeExt.jl:1 in expression starting at stdin:5 ✗ DynamicPPL → DynamicPPLMooncakeExt 4946.9 ms ✓ DynamicPPL → DynamicPPLChainRulesCoreExt 8891.9 ms ✓ DynamicPPL → DynamicPPLMCMCChainsExt 5464.8 ms ✓ DynamicPPL → DynamicPPLForwardDiffExt 4533.0 ms ✓ OptimizationOptimJL 15557.9 ms ✓ Turing 11787.6 ms ✓ Turing → TuringDynamicHMCExt 32 dependencies successfully precompiled in 240 seconds. 323 already precompiled. Precompilation completed after 258.04s ################################################################################ # Testing # Testing Turing Test Could not use exact versions of packages in manifest, re-resolving. Note: if you do not check your manifest file into source control, then you can probably ignore this message. However, if you do check your manifest file into source control, then you probably want to pass the `allow_reresolve = false` kwarg when calling the `Pkg.test` function. Updating `/tmp/jl_fOF8mm/Project.toml` [4c88cf16] + Aqua v0.8.14 [aaaa29a8] + Clustering v0.15.8 [bbc10e6e] + DynamicHMC v3.6.0 [26cc04aa] + FiniteDifferences v0.12.33 [09f84164] + HypothesisTests v0.11.6 ⌅ [da2b9cff] + Mooncake v0.4.203 [3e6eede4] + OptimizationBBO v0.4.5 [4e6fcdb7] + OptimizationNLopt v0.3.8 [37e2e3b7] + ReverseDiff v1.16.2 [860ef19b] + StableRNGs v1.0.4 [a759f4b9] + TimerOutputs v0.5.29 [fce5fe82] + Turing v0.42.8 [44cfe95a] ~ Pkg ⇒ v1.14.0 Updating `/tmp/jl_fOF8mm/Manifest.toml` [0bf59076] + AdvancedHMC v0.8.3 [4c88cf16] + Aqua v0.8.14 [ec485272] + ArnoldiMethod v0.4.0 [a134a8b2] + BlackBoxOptim v0.6.3 [fa961155] + CEnum v0.5.0 [a9c8d775] + CPUTime v1.0.0 [aaaa29a8] + Clustering v0.15.8 ⌃ [a0c0ee7d] ↓ DifferentiationInterface v0.7.16 ⇒ v0.7.15 [8d63f2c5] + DispatchDoctor v0.4.28 [b4f34e82] + Distances v0.10.12 [bbc10e6e] + DynamicHMC v3.6.0 [cad2338a] + EllipticalSliceSampling v2.0.0 [26cc04aa] + FiniteDifferences v0.12.33 [86223c79] + Graphs v1.13.4 [09f84164] + HypothesisTests v0.11.6 [d25df0c9] + Inflate v0.1.5 ⌅ [682c06a0] ↓ JSON v1.4.0 ⇒ v0.21.4 [1fad7336] + LazyStack v0.1.3 [6f1fad26] + Libtask v0.9.10 [dbe65cb8] + MistyClosures v2.1.0 ⌅ [da2b9cff] + Mooncake v0.4.203 [76087f3c] + NLopt v1.2.1 [b8a86587] + NearestNeighbors v0.4.27 [3e6eede4] + OptimizationBBO v0.4.5 [4e6fcdb7] + OptimizationNLopt v0.3.8 [65ce6f38] + PackageExtensionCompat v1.0.2 [37e2e3b7] + ReverseDiff v1.16.2 [708f8203] + Richardson v1.4.2 [699a6c99] + SimpleTraits v0.9.5 [d4ead438] + SpatialIndexing v0.1.6 [860ef19b] + StableRNGs v1.0.4 [5e0ebb24] + Strided v2.3.2 [4db3bf67] + StridedViews v0.4.3 [ec057cc2] - StructUtils v2.6.2 [02d47bb6] + TensorCast v0.4.9 [a759f4b9] + TimerOutputs v0.5.29 [24ddb15e] + TransmuteDims v0.1.17 [9d95972d] + TupleTools v1.6.0 [fce5fe82] + Turing v0.42.8 [079eb43e] + NLopt_jll v2.10.0+0 [0dad84c5] + ArgTools v1.1.2 [f43a241f] + Downloads v1.7.0 [b27032c2] + LibCURL v1.0.0 [76f85450] + LibGit2 v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] ~ Pkg ⇒ v1.14.0 [a4e569a6] + Tar v1.10.0 [deac9b47] + LibCURL_jll v8.18.0+0 [e37daf67] + LibGit2_jll v1.9.2+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.12.2 [458c3c95] + OpenSSL_jll v3.5.4+0 [efcefdf7] + PCRE2_jll v10.47.0+0 [83775a58] + Zlib_jll v1.3.1+2 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850ede] + nghttp2_jll v1.68.0+1 [3f19e933] + p7zip_jll v17.7.0+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Test Successfully re-resolved Status `/tmp/jl_fOF8mm/Project.toml` [47edcb42] ADTypes v1.21.0 [80f14c24] AbstractMCMC v5.14.0 ⌅ [7a57a42e] AbstractPPL v0.13.6 [5b7e9947] AdvancedMH v0.8.9 [576499cb] AdvancedPS v0.7.2 [b5ca4192] AdvancedVI v0.6.2 [4c88cf16] Aqua v0.8.14 [198e06fe] BangBang v0.4.7 [76274a88] Bijectors v0.15.16 [aaaa29a8] Clustering v0.15.8 [861a8166] Combinatorics v1.1.0 [31c24e10] Distributions v0.25.123 [ced4e74d] DistributionsAD v0.6.58 [bbc10e6e] DynamicHMC v3.6.0 [366bfd00] DynamicPPL v0.39.13 [26cc04aa] FiniteDifferences v0.12.33 [f6369f11] ForwardDiff v1.3.2 [09f84164] HypothesisTests v0.11.6 [6fdf6af0] LogDensityProblems v2.2.0 [996a588d] LogDensityProblemsAD v1.13.1 [c7f686f2] MCMCChains v7.7.0 ⌅ [da2b9cff] Mooncake v0.4.203 [86f7a689] NamedArrays v0.10.5 [7f7a1694] Optimization v5.4.0 [3e6eede4] OptimizationBBO v0.4.5 [4e6fcdb7] OptimizationNLopt v0.3.8 [36348300] OptimizationOptimJL v0.4.9 [90014a1f] PDMats v0.11.37 [37e2e3b7] ReverseDiff v1.16.2 [276daf66] SpecialFunctions v2.6.1 [860ef19b] StableRNGs v1.0.4 [2913bbd2] StatsBase v0.34.10 [4c63d2b9] StatsFuns v1.5.2 [a759f4b9] TimerOutputs v0.5.29 [fce5fe82] Turing v0.42.8 [37e2e46d] LinearAlgebra v1.13.0 [44cfe95a] Pkg v1.14.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_fOF8mm/Manifest.toml` [47edcb42] ADTypes v1.21.0 [621f4979] AbstractFFTs v1.5.0 [80f14c24] AbstractMCMC v5.14.0 ⌅ [7a57a42e] AbstractPPL v0.13.6 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.43 [79e6a3ab] Adapt v4.4.0 [0bf59076] AdvancedHMC v0.8.3 [5b7e9947] AdvancedMH v0.8.9 [576499cb] AdvancedPS v0.7.2 [b5ca4192] AdvancedVI v0.6.2 [66dad0bd] AliasTables v1.1.3 [4c88cf16] Aqua v0.8.14 [dce04be8] ArgCheck v2.5.0 [ec485272] ArnoldiMethod v0.4.0 [4fba245c] ArrayInterface v7.22.0 [13072b0f] AxisAlgorithms v1.1.0 [39de3d68] AxisArrays v0.4.8 [198e06fe] BangBang v0.4.7 [9718e550] Baselet v0.1.1 [76274a88] Bijectors v0.15.16 [a134a8b2] BlackBoxOptim v0.6.3 [fa961155] CEnum v0.5.0 [a9c8d775] CPUTime v1.0.0 [082447d4] ChainRules v1.72.6 [d360d2e6] ChainRulesCore v1.26.0 [0ca39b1e] Chairmarks v1.3.1 [9e997f8a] ChangesOfVariables v0.1.10 [aaaa29a8] Clustering v0.15.8 [861a8166] Combinatorics v1.1.0 [38540f10] CommonSolve v0.2.6 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.18.1 [a33af91c] CompositionsBase v0.1.2 [88cd18e8] ConsoleProgressMonitor v0.1.2 [187b0558] ConstructionBase v1.6.0 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [864edb3b] DataStructures v0.19.3 [e2d170a0] DataValueInterfaces v1.0.0 [244e2a9f] DefineSingletons v0.1.2 [8bb1440f] DelimitedFiles v1.9.1 [b429d917] DensityInterface v0.4.0 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.15.1 ⌃ [a0c0ee7d] DifferentiationInterface v0.7.15 [8d63f2c5] DispatchDoctor v0.4.28 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.123 [ced4e74d] DistributionsAD v0.6.58 [ffbed154] DocStringExtensions v0.9.5 [bbc10e6e] DynamicHMC v3.6.0 [366bfd00] DynamicPPL v0.39.13 [cad2338a] EllipticalSliceSampling v2.0.0 [4e289a0a] EnumX v1.0.6 [e2ba6199] ExprTools v0.1.10 [55351af7] ExproniconLite v0.10.14 [b86e33f2] FFTA v0.3.1 [9aa1b823] FastClosures v0.3.2 [1a297f60] FillArrays v1.16.0 [6a86dc24] FiniteDiff v2.29.0 [26cc04aa] FiniteDifferences v0.12.33 [f6369f11] ForwardDiff v1.3.2 [069b7b12] FunctionWrappers v1.1.3 [77dc65aa] FunctionWrappersWrappers v0.1.3 [d9f16b24] Functors v0.5.2 [46192b85] GPUArraysCore v0.2.0 [86223c79] Graphs v1.13.4 [34004b35] HypergeometricFunctions v0.3.28 [09f84164] HypothesisTests v0.11.6 [d25df0c9] Inflate v0.1.5 [22cec73e] InitialValues v0.3.1 [18e54dd8] IntegerMathUtils v0.1.3 [a98d9a8b] Interpolations v0.16.2 [8197267c] IntervalSets v0.7.13 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [c8e1da08] IterTools v1.10.0 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.7.1 ⌅ [682c06a0] JSON v0.21.4 [ae98c720] Jieko v0.2.1 [5ab0869b] KernelDensity v0.6.11 [b964fa9f] LaTeXStrings v1.4.0 [1fad7336] LazyStack v0.1.3 [1d6d02ad] LeftChildRightSiblingTrees v0.2.1 [6f1fad26] Libtask v0.9.10 [d3d80556] LineSearches v7.6.0 [6fdf6af0] LogDensityProblems v2.2.0 [996a588d] LogDensityProblemsAD v1.13.1 [2ab3a3ac] LogExpFunctions v0.3.29 [e6f89c97] LoggingExtras v1.2.0 [c7f686f2] MCMCChains v7.7.0 [be115224] MCMCDiagnosticTools v0.3.16 [e80e1ace] MLJModelInterface v1.12.1 [1914dd2f] MacroTools v0.5.16 [dbb5928d] MappedArrays v0.4.3 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [dbe65cb8] MistyClosures v2.1.0 ⌅ [da2b9cff] Mooncake v0.4.203 [2e0e35c7] Moshi v0.3.7 [46d2c3a1] MuladdMacro v0.2.4 [d41bc354] NLSolversBase v8.0.0 [76087f3c] NLopt v1.2.1 [77ba4419] NaNMath v1.1.3 [86f7a689] NamedArrays v0.10.5 [c020b1a1] NaturalSort v1.0.0 [b8a86587] NearestNeighbors v0.4.27 [6fe1bfb0] OffsetArrays v1.17.0 [429524aa] Optim v2.0.1 [3bd65402] Optimisers v0.4.7 [7f7a1694] Optimization v5.4.0 [3e6eede4] 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Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... [ Info: [Turing]: progress logging is disabled globally Persistent tasks: Test Failed at /home/pkgeval/.julia/packages/Aqua/MCcFg/src/persistent_tasks.jl:38 Expression: !(has_persistent_tasks(package; kwargs...)) Evaluated: !(has_persistent_tasks(Base.PkgId(Base.UUID("fce5fe82-541a-59a6-adf8-730c64b5f9a0"), "Turing"))) Stacktrace: [1] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:782 [inlined] [2] test_persistent_tasks(package::Base.PkgId; broken::Bool, kwargs::@Kwargs{}) @ Aqua ~/.julia/packages/Aqua/MCcFg/src/persistent_tasks.jl:38 [3] test_persistent_tasks @ ~/.julia/packages/Aqua/MCcFg/src/persistent_tasks.jl:34 [inlined] [4] #test_persistent_tasks#57 @ ~/.julia/packages/Aqua/MCcFg/src/persistent_tasks.jl:43 [inlined] [5] test_persistent_tasks @ ~/.julia/packages/Aqua/MCcFg/src/persistent_tasks.jl:42 [inlined] [6] macro expansion @ ~/.julia/packages/Aqua/MCcFg/src/Aqua.jl:109 [inlined] [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [8] test_all(testtarget::Module; ambiguities::Bool, unbound_args::Bool, undefined_exports::Bool, project_extras::Bool, stale_deps::Bool, deps_compat::Bool, piracies::Bool, persistent_tasks::Bool, undocumented_names::Bool) @ Aqua ~/.julia/packages/Aqua/MCcFg/src/Aqua.jl:109 ERROR: LoadError: UndefVarError: `WorldView` not defined in `Compiler` Suggestion: check for spelling errors or missing imports. Stacktrace:  [1] getproperty(x::Module, f::Symbol)  @ Base ./Base_compiler.jl:50  [2] top-level scope  @ ~/.julia/packages/Mooncake/qc0nh/src/interpreter/abstract_interpretation.jl:89  [3] include(mapexpr::Function, mod::Module, _path::String)  @ Base ./Base.jl:310  [4] top-level scope  @ ~/.julia/packages/Mooncake/qc0nh/src/Mooncake.jl:124  [5] include(mod::Module, _path::String)  @ Base ./Base.jl:309  [6] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3309  [7] top-level scope  @ stdin:5  [8] eval(m::Module, e::Any)  @ Core ./boot.jl:489  [9] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3151  [10] include_string  @ ./loading.jl:3161 [inlined]  [11] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:342  [12] _start()  @ Base ./client.jl:585 in expression starting at /home/pkgeval/.julia/packages/Mooncake/qc0nh/src/interpreter/abstract_interpretation.jl:89 in expression starting at /home/pkgeval/.julia/packages/Mooncake/qc0nh/src/Mooncake.jl:1 in expression starting at stdin:5 1 dependency had output during precompilation: ┌ Mooncake │ [Output was shown above] └ AD: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/runtests.jl:37 Got exception outside of a @test LoadError: The following 1 package failed to precompile: Mooncake Failed to precompile Mooncake [da2b9cff-9c12-43a0-ae48-6db2b0edb7d6] to "/home/pkgeval/.julia/compiled/v1.14/Mooncake/jl_gdAlQp" (ProcessExited(1)). in expression starting at /home/pkgeval/.julia/packages/Turing/EdqqP/test/ad.jl:1 constructor: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/essential/container.jl:20 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{Main.ContainerTests.var"#test#test##0", DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ContainerTests.var"#test#test##0", DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ContainerTests.var"#test#test##0", DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/essential/container.jl:10 [11] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [12] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/essential/container.jl:21 [inlined] [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [14] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/essential/container.jl:25 [inlined] [15] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [16] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [18] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:42 [inlined] [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [20] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [21] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [22] top-level scope @ none:6 [23] eval(m::Module, e::Any) @ Core ./boot.jl:489 [24] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [25] _start() @ Base ./client.jl:585 fork: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/essential/container.jl:38 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{Main.ContainerTests.var"#normal#normal##0", DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ContainerTests.var"#normal#normal##0", DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ContainerTests.var"#normal#normal##0", DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/essential/container.jl:10 [11] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [12] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/essential/container.jl:39 [inlined] [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [14] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/essential/container.jl:51 [inlined] [15] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [16] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [18] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:42 [inlined] [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [20] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [21] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [22] top-level scope @ none:6 [23] eval(m::Module, e::Any) @ Core ./boot.jl:489 [24] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [25] _start() @ Base ./client.jl:585 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Info: Found initial step size └ ϵ = 6.4 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Warning: Only a single process available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:732 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Info: Found initial step size └ ϵ = 1.625 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. [ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. [ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead. ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:549 ┌ Info: Found initial step size └ ϵ = 1.6 PG: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/callbacks.jl:25 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.CallbacksTests.test_normals), DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}, y::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:y, typeof(identity)}, Int64}, Vector{Distributions.IsoNormal}, Vector{AbstractPPL.VarName{:y, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}, y::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:y, typeof(identity)}, Int64}, Vector{Distributions.IsoNormal}, Vector{AbstractPPL.VarName{:y, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{typeof(Main.CallbacksTests.test_normals), DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}, y::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:y, typeof(identity)}, Int64}, Vector{Distributions.IsoNormal}, Vector{AbstractPPL.VarName{:y, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}, y::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:y, typeof(identity)}, Int64}, Vector{Distributions.IsoNormal}, Vector{AbstractPPL.VarName{:y, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{typeof(Main.CallbacksTests.test_normals), DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}, y::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:y, typeof(identity)}, Int64}, Vector{Distributions.IsoNormal}, Vector{AbstractPPL.VarName{:y, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}, y::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:y, typeof(identity)}, Int64}, Vector{Distributions.IsoNormal}, Vector{AbstractPPL.VarName{:y, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}, y::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:y, typeof(identity)}, Int64}, Vector{Distributions.IsoNormal}, Vector{AbstractPPL.VarName{:y, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}, y::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:y, typeof(identity)}, Int64}, Vector{Distributions.IsoNormal}, Vector{AbstractPPL.VarName{:y, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}, y::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:y, typeof(identity)}, Int64}, Vector{Distributions.IsoNormal}, Vector{AbstractPPL.VarName{:y, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}, y::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:y, typeof(identity)}, Int64}, Vector{Distributions.IsoNormal}, Vector{AbstractPPL.VarName{:y, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(AbstractMCMC.step), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.CallbacksTests.test_normals), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [17] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/callbacks.jl:11 [18] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [19] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/callbacks.jl:26 [inlined] [20] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [21] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/callbacks.jl:26 [inlined] [22] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [23] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [24] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [25] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:46 [inlined] [26] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [27] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [28] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [29] top-level scope @ none:6 [30] eval(m::Module, e::Any) @ Core ./boot.jl:489 [31] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [32] _start() @ Base ./client.jl:585 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Warning: It looks like you are using `Threads.@threads` in your model definition. │ │ Note that since version 0.39 of DynamicPPL, threadsafe evaluation of models is disabled by default. If you need it, you will need to explicitly enable it by creating the model, and then running `model = setthreadsafe(model, true)`. │ │ Threadsafe model evaluation is only needed when parallelising tilde-statements (not arbitrary Julia code), and avoiding it can often lead to significant performance improvements. │ │ Please see https://turinglang.org/docs/usage/threadsafe-evaluation/ for more details of when threadsafe evaluation is actually required. └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/compiler.jl:383 models: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:27 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#normal#normal##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ParticleMCMCTests.var"#normal#normal##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ParticleMCMCTests.var"#normal#normal##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#57#58"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#57#58"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; nparticles::Int64, discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:178 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool, nparticles::Int64}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:163 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, nparticles::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [inlined] [18] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [19] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [20] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [21] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [22] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [23] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [24] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [25] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [26] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [27] kwcall(::@NamedTuple{chain_type::UnionAll, initial_params::DynamicPPL.InitFromPrior, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [28] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; check_model::Bool, chain_type::Type, initial_params::DynamicPPL.InitFromPrior, progress::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:128 [29] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:113 [inlined] [30] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [31] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [32] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:13 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:28 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:36 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [38] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [44] top-level scope @ none:6 [45] eval(m::Module, e::Any) @ Core ./boot.jl:489 [46] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [47] _start() @ Base ./client.jl:585 chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:53 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#57#58"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#57#58"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; nparticles::Int64, discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:178 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool, nparticles::Int64}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:163 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, nparticles::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [inlined] [18] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [19] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [20] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [21] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [22] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [23] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [24] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [25] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [26] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [27] kwcall(::@NamedTuple{chain_type::UnionAll, initial_params::DynamicPPL.InitFromPrior, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [28] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; check_model::Bool, chain_type::Type, initial_params::DynamicPPL.InitFromPrior, progress::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:128 [29] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:113 [inlined] [30] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [31] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [32] test_chain_logp_metadata(spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/EdqqP/test/test_utils/sampler.jl:22 [33] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:13 [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:54 [inlined] [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:54 [inlined] [38] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [39] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:46 [inlined] [42] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [43] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [44] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [45] top-level scope @ none:6 [46] eval(m::Module, e::Any) @ Core ./boot.jl:489 [47] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [48] _start() @ Base ./client.jl:585 logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:57 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#test#test##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ParticleMCMCTests.var"#test#test##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ParticleMCMCTests.var"#test#test##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#57#58"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#57#58"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; nparticles::Int64, discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:178 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool, nparticles::Int64}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:163 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool, nparticles::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [inlined] [18] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [19] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [20] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [21] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [22] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [23] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [24] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [25] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [26] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [27] kwcall(::@NamedTuple{chain_type::UnionAll, initial_params::DynamicPPL.InitFromPrior, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [28] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; check_model::Bool, chain_type::Type, initial_params::DynamicPPL.InitFromPrior, progress::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:128 [29] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:113 [inlined] [30] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [31] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [32] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:13 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:58 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:70 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [38] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [44] top-level scope @ none:6 [45] eval(m::Module, e::Any) @ Core ./boot.jl:489 [46] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [47] _start() @ Base ./client.jl:585 ┌ Warning: It looks like you are using `Threads.@threads` in your model definition. │ │ Note that since version 0.39 of DynamicPPL, threadsafe evaluation of models is disabled by default. If you need it, you will need to explicitly enable it by creating the model, and then running `model = setthreadsafe(model, true)`. │ │ Threadsafe model evaluation is only needed when parallelising tilde-statements (not arbitrary Julia code), and avoiding it can often lead to significant performance improvements. │ │ Please see https://turinglang.org/docs/usage/threadsafe-evaluation/ for more details of when threadsafe evaluation is actually required. └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/compiler.jl:383 chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:115 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [inlined] [18] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [19] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [20] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [21] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [22] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [23] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [24] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [25] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [26] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [27] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [28] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [29] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [30] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [31] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [32] test_chain_logp_metadata(spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/EdqqP/test/test_utils/sampler.jl:22 [33] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:97 [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:116 [inlined] [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:116 [inlined] [38] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [39] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:46 [inlined] [42] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [43] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [44] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [45] top-level scope @ none:6 [46] eval(m::Module, e::Any) @ Core ./boot.jl:489 [47] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [48] _start() @ Base ./client.jl:585 logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:119 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#test#test##1", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ParticleMCMCTests.var"#test#test##1", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ParticleMCMCTests.var"#test#test##1", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [inlined] [18] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [19] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [20] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [21] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [22] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [23] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [24] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [25] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [26] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [27] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [28] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [29] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [30] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:97 [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:120 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:130 [inlined] [35] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [36] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:46 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ none:6 [43] eval(m::Module, e::Any) @ Core ./boot.jl:489 [44] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [45] _start() @ Base ./client.jl:585 reference particle: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:140 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [inlined] [18] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [19] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [20] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [21] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [22] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [23] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [24] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [25] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [26] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [27] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [28] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [29] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [30] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [31] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [32] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:97 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:141 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:141 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [38] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [44] top-level scope @ none:6 [45] eval(m::Module, e::Any) @ Core ./boot.jl:489 [46] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [47] _start() @ Base ./client.jl:585 addlogprob leads to reweighting: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:146 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [inlined] [18] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [19] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [20] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [21] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [22] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [23] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [24] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [25] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [26] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [27] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [28] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [29] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [30] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:97 [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:149 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:159 [inlined] [35] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [36] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:46 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ none:6 [43] eval(m::Module, e::Any) @ Core ./boot.jl:489 [44] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [45] _start() @ Base ./client.jl:585 keyword argument handling: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:165 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Core.kwcall), @NamedTuple{n::Float64}, Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{n::Float64}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] TapedTask @ ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [inlined] [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64}, @NamedTuple{n::Float64}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64}, @NamedTuple{n::Float64}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [inlined] [18] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [19] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [20] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [21] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [22] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [23] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [24] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [25] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [26] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [27] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [28] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [29] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#kwarg_demo#kwarg_demo##0"{Main.ParticleMCMCTests.var"#kwarg_demo#2#5"}, (:y,), (:n,), (), Tuple{Float64}, Tuple{Float64}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [30] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:97 [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:166 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/particle_mcmc.jl:174 [inlined] [35] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [36] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:46 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ none:6 [43] eval(m::Module, e::Any) @ Core ./boot.jl:489 [44] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [45] _start() @ Base ./client.jl:585 ┌ Warning: [DynamicPPL] attempt to link a linked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:919 ┌ Warning: [DynamicPPL] attempt to link a linked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:919 [ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 2.062630559118322) [ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.1614954615219162) [ Info: Testing emcee with large number of iterations ┌ Warning: [DynamicPPL] attempt to link a linked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:919 ┌ Warning: [DynamicPPL] attempt to link a linked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:919 [ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 2.0765449812082557) [ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.1697615669122199) ┌ Warning: [DynamicPPL] attempt to link a linked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:919 ┌ Warning: [DynamicPPL] attempt to link a linked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:919 ┌ Warning: [DynamicPPL] attempt to link a linked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:919 ┌ Warning: [DynamicPPL] attempt to link a linked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:919 ┌ Warning: [DynamicPPL] attempt to link a linked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:919 ┌ Warning: [DynamicPPL] attempt to link a linked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:919 [ Info: Starting ESS tests [ Info: Starting ESS inference tests [ Info: (symbol = :m, exact = 0.8, evaluated = 0.8173726888512937) [ Info: (symbol = "m[1]", exact = 0.0, evaluated = -0.025360856826163417) [ Info: (symbol = "m[2]", exact = 0.8, evaluated = 0.8069838968220362) gdemo with CSMC + ESS: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/ess.jl:61 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [18] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [20] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [21] step_warmup @ ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [inlined] [22] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [23] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [24] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [25] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [26] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [27] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [28] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [29] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [30] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [31] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [32] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [33] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [34] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/ess.jl:14 [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/ess.jl:48 [inlined] [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/ess.jl:62 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/ess.jl:63 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [43] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [44] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:46 [inlined] [45] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [46] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [47] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [48] top-level scope @ none:6 [49] eval(m::Module, e::Any) @ Core ./boot.jl:489 [50] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [51] _start() @ Base ./client.jl:585 MoGtest_default with CSMC + ESS: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/ess.jl:67 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [18] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}, vi::DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}, vi::DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [20] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [21] step_warmup @ ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [inlined] [22] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [23] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [24] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [25] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [26] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [27] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [28] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [29] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [30] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [31] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [32] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [33] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [34] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/ess.jl:14 [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/ess.jl:48 [inlined] [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/ess.jl:68 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/ess.jl:73 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [43] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [44] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:46 [inlined] [45] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [46] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [47] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [48] top-level scope @ none:6 [49] eval(m::Module, e::Any) @ Core ./boot.jl:489 [50] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [51] _start() @ Base ./client.jl:585 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Info: Found initial step size └ ϵ = 3.2 WARNING: Method definition (::Type{GibbsTests.Wrapper{T} where T})(T) where {T<:Real} in module GibbsTests at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:35 overwritten at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:193. WARNING: Method definition (::Type{GibbsTests.Wrapper{T<:Real}})(Any) in module GibbsTests at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:35 overwritten at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:193. Sampler call order: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:135 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{Main.GibbsTests.var"#test_model#test_model##1", DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Int64, DynamicPPL.TypeWrap{Vector{Float64}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{Main.GibbsTests.var"#test_model#test_model##1", DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{Main.GibbsTests.var"#test_model#test_model##1", DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] step(::Random.TaskLocalRNG, ::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ::Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}) @ Main.GibbsTests ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:178 [18] step @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:170 [inlined] [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [20] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}, Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 ┌ [21] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}, Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}}) │ @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 ├ [22] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) │ @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:412 ╰───── repeated 2 times [25] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [26] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [27] kwcall(::@NamedTuple{num_warmup::Int64, discard_sample::Bool, initial_params::DynamicPPL.InitFromPrior}, ::typeof(AbstractMCMC.step_warmup), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [28] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [29] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [30] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [31] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [32] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [33] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [34] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [35] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [36] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [37] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [38] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [39] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [40] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [41] sample(model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#952")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [42] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:138 [43] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [44] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:239 [inlined] [45] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [46] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [47] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [48] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:56 [inlined] [49] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [50] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [51] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [52] top-level scope @ none:6 [53] eval(m::Module, e::Any) @ Core ./boot.jl:489 [54] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [55] _start() @ Base ./client.jl:585 [ Info: Starting Gibbs tests Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:388 Test threw exception Expression: sample(gdemo_default, s2, N) isa MCMCChains.Chains MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [18] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [20] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [21] step_warmup @ ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [inlined] [22] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [23] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [24] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [25] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [26] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [27] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [28] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [29] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [30] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [31] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [32] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [33] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [34] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [35] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [36] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:361 [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:366 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:388 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:779 [inlined] Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:389 Test threw exception Expression: sample(gdemo_default, s3, N) isa MCMCChains.Chains MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [18] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [20] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [21] step_warmup @ ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [inlined] [22] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [23] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [24] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [25] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [26] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [27] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [28] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [29] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [30] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [31] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [32] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [33] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [34] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [35] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [36] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:361 [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:366 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:389 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:779 [inlined] Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:391 Test threw exception Expression: sample(gdemo_default, s5, N) isa MCMCChains.Chains MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [18] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, 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@NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), 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@NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [20] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [22] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:412 [23] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [24] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:412 [25] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [26] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [27] step_warmup @ ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [inlined] [28] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [29] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [30] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [31] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [32] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [33] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [34] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [35] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [36] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [37] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [38] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [39] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [40] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [41] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [42] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:361 [43] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [44] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:366 [inlined] [45] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [46] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:391 [inlined] [47] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:779 [inlined] Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:392 Test threw exception Expression: sample(gdemo_default, s6, N) isa MCMCChains.Chains MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [inlined] [18] #step_warmup#9 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/repeat_sampler.jl:80 [inlined] [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/repeat_sampler.jl:74 [20] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 [21] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation, DynamicPPL.LogDensityFunction{true, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, typeof(DynamicPPL.getlogjoint_internal), @NamedTuple{s::DynamicPPL.RangeAndLinked}, DifferentiationInterfaceForwardDiffExt.ForwardDiffGradientPrep{Tuple{typeof(DynamicPPL.logdensity_at), ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, Vector{Float64}, Tuple{DifferentiationInterface.Constant{Val{true}}, DifferentiationInterface.Constant{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}}, DifferentiationInterface.Constant{typeof(DynamicPPL.getlogjoint_internal)}, DifferentiationInterface.Constant{@NamedTuple{s::DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{Dict{AbstractPPL.VarName, DynamicPPL.RangeAndLinked}}, DifferentiationInterface.Constant{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 1}}}, NTuple{6, Nothing}}, Vector{Float64}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [22] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:412 [23] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [24] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [25] step_warmup @ ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [inlined] [26] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [27] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [28] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [29] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [30] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [31] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [32] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [33] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [34] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [35] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [36] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [37] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [38] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [39] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [40] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:361 [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:366 [inlined] [43] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [44] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:392 [inlined] [45] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:779 [inlined] CSMC and HMC on gdemo: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:398 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [18] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [20] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [21] step_warmup @ ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [inlined] [22] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [23] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [24] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [25] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [26] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [27] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [28] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [29] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [30] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [31] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [32] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [33] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [34] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [35] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [36] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:361 [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:398 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:399 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:400 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [44] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [45] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [46] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:56 [inlined] [47] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [48] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [49] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [50] top-level scope @ none:6 [51] eval(m::Module, e::Any) @ Core ./boot.jl:489 [52] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [53] _start() @ Base ./client.jl:585 ┌ Info: Found initial step size └ ϵ = 0.8 [ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 2.06495666218506) [ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.1459818573445812) CSMC and ESS on gdemo: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:412 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [18] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [20] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [21] step_warmup @ ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [inlined] [22] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [23] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [24] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [25] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [26] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [27] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [28] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [29] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [30] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [31] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [32] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [33] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [34] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [35] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [36] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:361 [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:398 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:413 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:414 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [44] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [45] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [46] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:56 [inlined] [47] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [48] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [49] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [50] top-level scope @ none:6 [51] eval(m::Module, e::Any) @ Core ./boot.jl:489 [52] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [53] _start() @ Base ./client.jl:585 CSMC on gdemo: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:419 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [inlined] [18] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [19] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [20] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [21] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [22] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [23] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [24] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [25] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [26] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [27] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [28] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [29] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [30] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [31] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [32] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:361 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:398 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:420 [inlined] [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:421 [inlined] [39] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [40] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:56 [inlined] [43] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [44] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [45] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [46] top-level scope @ none:6 [47] eval(m::Module, e::Any) @ Core ./boot.jl:489 [48] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [49] _start() @ Base ./client.jl:585 PG and HMC on MoGtest_default: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:425 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [18] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, vi::DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, vi::DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [20] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [21] step_warmup @ ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [inlined] [22] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [23] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [24] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [25] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [26] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [27] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [28] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [29] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [30] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [31] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [32] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [33] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [34] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [35] sample(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [36] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:361 [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:398 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:426 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:430 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [44] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [45] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [46] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:56 [inlined] [47] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [48] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [49] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [50] top-level scope @ none:6 [51] eval(m::Module, e::Any) @ Core ./boot.jl:489 [52] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [53] _start() @ Base ./client.jl:585 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: 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[DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL 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an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 ┌ Warning: [DynamicPPL] attempt to invlink an invlinked vi └ @ DynamicPPL ~/.julia/packages/DynamicPPL/GG1GP/src/varinfo.jl:1023 [ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 2.0847976379405475) [ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.1286327618566863) Multiple overlapping samplers on MoGtest_default: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:448 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [18] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [20] step_warmup(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [21] step_warmup @ ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [inlined] [22] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [23] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [24] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [25] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [26] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [27] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [28] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [29] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [30] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [31] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [32] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [33] sample @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [inlined] [34] #sample#1 @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:73 [inlined] [35] sample(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{7, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, Turing.Inference.RepeatSampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.ESS, Turing.Inference.ESS, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:70 [36] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:361 [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:398 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:449 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:458 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [44] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [45] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [46] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:56 [inlined] [47] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [48] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [49] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [50] top-level scope @ none:6 [51] eval(m::Module, e::Any) @ Core ./boot.jl:489 [52] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [53] _start() @ Base ./client.jl:585 Inserting @addlogprob!: Error During Test at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:619 Got exception outside of a @test MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64) The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833 Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any) @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866 Stacktrace: [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115 [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52 [3] optimise_ir!(ir::Compiler.IRCode) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40 [4] build_callable(sig::Type{Tuple{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303 [7] AdvancedPS.LibtaskModel(f::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:44 [8] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, Tuple{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, false}, DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}, @NamedTuple{}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/ZHu61/ext/AdvancedPSLibtaskExt.jl:107 [9] AdvancedPS.Trace(model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, varinfo::DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:475 [10] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64) @ Turing.Inference ./none:-1 [11] iterate @ ./generator.jl:48 [inlined] [12] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}) @ Base ./array.jl:828 [13] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; discard_sample::Bool, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:300 [14] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(Turing.Inference.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/particle_mcmc.jl:286 [15] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64, discard_sample::Bool}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:182 [16] step @ ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:164 [inlined] [17] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64, discard_sample::Bool}, ::typeof(AbstractMCMC.step_warmup), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:b, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, false}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/interface.jl:118 [18] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::typeof(AbstractMCMC.step_warmup), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}, vi::DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:394 [19] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, num_warmup::Int64}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}}, samplers::Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}, vi::DynamicPPL.VarInfo{@NamedTuple{b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, BitVector}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:371 [20] step_warmup(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}; initial_params::DynamicPPL.InitFromPrior, discard_sample::Bool, kwargs::@Kwargs{num_warmup::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:349 [21] step_warmup @ ~/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:337 [inlined] [22] #_step_or_step_warmup#25 @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:126 [inlined] [23] _step_or_step_warmup @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] [24] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:223 [inlined] [25] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, Int64, Float64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:134 [26] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:542 [27] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}}) @ Base.CoreLogging ./logging/logging.jl:653 [28] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:157 [29] macro expansion @ ~/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:133 [inlined] [30] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 [31] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll, discard_initial::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 [32] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{discard_initial::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [33] kwcall(::@NamedTuple{discard_initial::Int64}, ::typeof(StatsBase.sample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#dynamic_bernoulli_2#dynamic_bernoulli_2##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 [34] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:361 [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:582 [inlined] [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:621 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:636 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:33 [43] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [44] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:56 [inlined] [45] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2244 [inlined] [46] macro expansion @ ~/.julia/packages/Turing/EdqqP/test/runtests.jl:25 [inlined] [47] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [48] top-level scope @ none:6 [49] eval(m::Module, e::Any) @ Core ./boot.jl:489 [50] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [51] _start() @ Base ./client.jl:585 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Info: Found initial step size └ ϵ = 0.8 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile. --trace-compile is enabled during profile collection. ====================================================================================== cmd: /opt/julia/bin/julia 136 running 1 of 1 signal (10): User defined signal 1 gc_sweep_page at /source/src/gc-stock.c:925 gc_sweep_pool_page at /source/src/gc-stock.c:995 [inlined] gc_sweep_pool_parallel at /source/src/gc-stock.c:1291 gc_sweep_pool at /source/src/gc-stock.c:1415 [inlined] _jl_gc_collect at /source/src/gc-stock.c:3202 ijl_gc_collect at /source/src/gc-stock.c:3510 maybe_collect at /source/src/gc-stock.c:356 [inlined] jl_gc_small_alloc_inner at /source/src/gc-stock.c:733 jl_gc_small_alloc_noinline at /source/src/gc-stock.c:791 [inlined] jl_gc_alloc_ at /source/src/gc-stock.c:805 arg_tuple at /source/src/builtins.c:948 macro expansion at /home/pkgeval/.julia/packages/DynamicPPL/GG1GP/src/compiler.jl:613 [inlined] demo_assume_submodel_observe_index_literal at /home/pkgeval/.julia/packages/DynamicPPL/GG1GP/src/test_utils/models.jl:451 _evaluate!! at /home/pkgeval/.julia/packages/DynamicPPL/GG1GP/src/model.jl:997 [inlined] evaluate!! at /home/pkgeval/.julia/packages/DynamicPPL/GG1GP/src/model.jl:983 [inlined] init!! at /home/pkgeval/.julia/packages/DynamicPPL/GG1GP/src/model.jl:938 [inlined] init!! at /home/pkgeval/.julia/packages/DynamicPPL/GG1GP/src/model.jl:943 [inlined] logdensity_at at /home/pkgeval/.julia/packages/DynamicPPL/GG1GP/src/logdensityfunction.jl:291 [inlined] FixTail at /home/pkgeval/.julia/packages/DifferentiationInterface/M8gIf/src/utils/context.jl:172 [inlined] vector_mode_dual_eval! at /home/pkgeval/.julia/packages/ForwardDiff/egQMG/src/apiutils.jl:24 [inlined] vector_mode_gradient! at /home/pkgeval/.julia/packages/ForwardDiff/egQMG/src/gradient.jl:105 gradient! at /home/pkgeval/.julia/packages/ForwardDiff/egQMG/src/gradient.jl:39 [inlined] value_and_gradient at /home/pkgeval/.julia/packages/DifferentiationInterface/M8gIf/ext/DifferentiationInterfaceForwardDiffExt/onearg.jl:419 [inlined] logdensity_and_gradient at /home/pkgeval/.julia/packages/DynamicPPL/GG1GP/src/logdensityfunction.jl:373 [inlined] #_#58 at ./operators.jl:1202 [inlined] Fix at ./operators.jl:1202 [inlined] ∂H∂θ at /home/pkgeval/.julia/packages/AdvancedHMC/kEVkt/src/hamiltonian.jl:46 [inlined] #step#10 at /home/pkgeval/.julia/packages/AdvancedHMC/kEVkt/src/integrator.jl:244 step at /home/pkgeval/.julia/packages/AdvancedHMC/kEVkt/src/integrator.jl:216 [inlined] sample_phasepoint at /home/pkgeval/.julia/packages/AdvancedHMC/kEVkt/src/trajectory.jl:337 [inlined] transition at /home/pkgeval/.julia/packages/AdvancedHMC/kEVkt/src/trajectory.jl:279 transition at /home/pkgeval/.julia/packages/AdvancedHMC/kEVkt/src/sampler.jl:57 [inlined] #step#31 at /home/pkgeval/.julia/packages/Turing/EdqqP/src/mcmc/hmc.jl:256 [inlined] step at /home/pkgeval/.julia/packages/Turing/EdqqP/src/mcmc/hmc.jl:241 unknown function (ip: 0x737b103d0151) at (unknown file) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 #gibbs_step_recursive#104 at /home/pkgeval/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:622 gibbs_step_recursive at /home/pkgeval/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:582 [inlined] gibbs_step_recursive at /home/pkgeval/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:582 [inlined] #step#102 at /home/pkgeval/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:439 step at /home/pkgeval/.julia/packages/Turing/EdqqP/src/mcmc/gibbs.jl:425 unknown function (ip: 0x737b103cd85f) at (unknown file) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 jl_apply at /source/src/julia.h:2285 [inlined] jl_f__apply_iterate at /source/src/builtins.c:876 #_step_or_step_warmup#25 at /home/pkgeval/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:0 _step_or_step_warmup at /home/pkgeval/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:124 [inlined] macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:296 [inlined] macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/oqm6Y/src/logging.jl:137 [inlined] #mcmcsample#27 at /home/pkgeval/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:204 mcmcsample at /home/pkgeval/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:164 #sample#2 at /home/pkgeval/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:87 [inlined] sample at /home/pkgeval/.julia/packages/Turing/EdqqP/src/mcmc/abstractmcmc.jl:76 unknown function (ip: 0x737b103b182b) at (unknown file) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 #62 at /home/pkgeval/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:684 unknown function (ip: 0x737b103b1638) at (unknown file) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 jl_apply at /source/src/julia.h:2285 [inlined] start_task at /source/src/task.c:1275 unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point. Disabling --trace-compile ============================================================== ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile. --trace-compile is enabled during profile collection. ====================================================================================== cmd: /opt/julia/bin/julia 1 running 0 of 1 signal (10): User defined signal 1 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457 wait at ./task.jl:1246 wait_forever at ./task.jl:1168 jfptr_wait_forever_0.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 jl_apply at /source/src/julia.h:2285 [inlined] start_task at /source/src/task.c:1275 unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point. Disabling --trace-compile ============================================================== ┌ Warning: There were no samples collected in one or more groups. │ This may be due to idle threads, or you may need to run your │ program longer (perhaps by running it multiple times), │ or adjust the delay between samples with `Profile.init()`. └ @ Profile /opt/julia/share/julia/stdlib/v1.14/Profile/src/Profile.jl:1361 Overhead ╎ [+additional indent] Count File:Line Function ========================================================= Thread 1 (default) Task 0x00007b957451fee0 Total snapshots: 462. Utilization: 0% ╎462 @Base/task.jl:1168 wait_forever() 461╎ 462 @Base/task.jl:1246 wait() ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/oqm6Y/src/sample.jl:544 [1] signal 15: Terminated in expression starting at /PkgEval.jl/scripts/evaluate.jl:214 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457 wait at ./task.jl:1246 wait_forever at ./task.jl:1168 jfptr_wait_forever_0.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 jl_apply at /source/src/julia.h:2285 [inlined] start_task at /source/src/task.c:1275 unknown function (ip: (nil)) at (unknown file) Allocations: 21176280 (Pool: 21175553; Big: 727); GC: 18 [136] signal 15: Terminated in expression starting at /home/pkgeval/.julia/packages/Turing/EdqqP/test/mcmc/gibbs.jl:360 jl_smallintset_lookup at /source/src/smallintset.c:122 jl_get_module_binding at /source/src/module.c:1600 ijl_is_const at /source/src/module.c:1881 isconst at ./runtime_internals.jl:461 [inlined] make_typealias at ./show.jl:643 show_typealias at ./show.jl:823 _show_type at ./show.jl:988 show at ./show.jl:983 jfptr_show_68.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_typeparams at ./show.jl:740 show_datatype at ./show.jl:1234 show_datatype at ./show.jl:1142 [inlined] _show_type at ./show.jl:991 show at ./show.jl:983 jfptr_show_68.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_at_namedtuple at ./show.jl:1247 unknown function (ip: 0x737b1303f05d) at (unknown file) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_datatype at ./show.jl:1220 show_datatype at ./show.jl:1142 [inlined] _show_type at ./show.jl:991 show at ./show.jl:983 jfptr_show_68.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_typeparams at ./show.jl:740 show_datatype at ./show.jl:1234 show_datatype at ./show.jl:1142 [inlined] _show_type at ./show.jl:991 show at ./show.jl:983 jfptr_show_68.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_typeparams at ./show.jl:740 show_datatype at ./show.jl:1234 show_datatype at ./show.jl:1142 [inlined] _show_type at ./show.jl:991 show at ./show.jl:983 jfptr_show_68.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_typeparams at ./show.jl:740 show_datatype at ./show.jl:1234 show_datatype at ./show.jl:1142 [inlined] _show_type at ./show.jl:991 show at ./show.jl:983 jfptr_show_68.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_typeparams at ./show.jl:740 show_datatype at ./show.jl:1234 show_datatype at ./show.jl:1142 [inlined] _show_type at ./show.jl:991 show at ./show.jl:983 jfptr_show_68.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_typeparams at ./show.jl:740 show_datatype at ./show.jl:1234 show_datatype at ./show.jl:1142 [inlined] _show_type at ./show.jl:991 show at ./show.jl:983 jfptr_show_68.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_typeparams at ./show.jl:740 show_typealias at ./show.jl:773 show_typealias at ./show.jl:826 _show_type at ./show.jl:988 show at ./show.jl:983 jfptr_show_68.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_typeparams at ./show.jl:740 show_datatype at ./show.jl:1234 show_datatype at ./show.jl:1142 [inlined] _show_type at ./show.jl:991 show at ./show.jl:983 jfptr_show_68.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_typeparams at ./show.jl:740 show_datatype at ./show.jl:1234 show_datatype at ./show.jl:1142 [inlined] _show_type at ./show.jl:991 show at ./show.jl:983 jfptr_show_68.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 show_datatype at ./show.jl:1205 show_datatype at ./show.jl:1142 [inlined] _show_type at ./show.jl:991 show at ./show.jl:983 [inlined] #sprint#423 at ./strings/io.jl:107 sprint at ./strings/io.jl:102 [inlined] #print_type_bicolor#497 at ./show.jl:2680 [inlined] print_type_bicolor at ./show.jl:2679 jfptr_print_type_bicolor_0.1 at /opt/julia/share/julia/compiled/v1.14/REPL/u0gqU_wscuQ.so (unknown line) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 #show_tuple_as_call#491 at ./show.jl:2547 show_tuple_as_call at ./show.jl:2514 [inlined] show_spec_sig at ./stacktraces.jl:325 show_spec_linfo at ./stacktraces.jl:289 #sprint#423 at ./strings/io.jl:109 [inlined] sprint at ./strings/io.jl:102 [inlined] tree_format at /source/usr/share/julia/stdlib/v1.14/Profile/src/Profile.jl:1042 print_tree at /source/usr/share/julia/stdlib/v1.14/Profile/src/Profile.jl:1254 tree at /source/usr/share/julia/stdlib/v1.14/Profile/src/Profile.jl:1308 print_group at /source/usr/share/julia/stdlib/v1.14/Profile/src/Profile.jl:389 #print#5 at /source/usr/share/julia/stdlib/v1.14/Profile/src/Profile.jl:331 print at /source/usr/share/julia/stdlib/v1.14/Profile/src/Profile.jl:271 [inlined] print at /source/usr/share/julia/stdlib/v1.14/Profile/src/Profile.jl:271 [inlined] print at /source/usr/share/julia/stdlib/v1.14/Profile/src/Profile.jl:271 _peek_report at /source/usr/share/julia/stdlib/v1.14/Profile/src/Profile.jl:94 unknown function (ip: 0x737b103d8ddf) at (unknown file) _jl_invoke at /source/src/gf.c:4113 [inlined] ijl_apply_generic at /source/src/gf.c:4310 jl_apply at /source/src/julia.h:2285 [inlined] jl_f_invokelatest at /source/src/builtins.c:889 profile_printing_listener at ./Base.jl:340 #start_profile_listener##0 at ./Base.jl:358 jfptr_YY.start_profile_listenerYY.YY.0_0.1 at /opt/julia/lib/julia/sys.so (unknown line) PkgEval terminated after 2742.96s: test duration exceeded the time limit