Package evaluation to test Turing on Julia 1.14.0-DEV.1299 (6d6224db99*) started at 2025-11-27T21:42:45.376 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.99s ################################################################################ # Installation # Installing Turing... Resolving package versions... Updating `~/.julia/environments/v1.14/Project.toml` [fce5fe82] + Turing v0.41.4 Updating `~/.julia/environments/v1.14/Manifest.toml` [47edcb42] + ADTypes v1.19.0 [621f4979] + AbstractFFTs v1.5.0 [80f14c24] + AbstractMCMC v5.10.0 [7a57a42e] + AbstractPPL v0.13.6 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.4.0 [0bf59076] + AdvancedHMC v0.8.3 [5b7e9947] + AdvancedMH v0.8.9 [576499cb] + AdvancedPS v0.7.0 ⌅ [b5ca4192] + AdvancedVI v0.4.1 [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 v0.4.6 [9718e550] + Baselet v0.1.1 [76274a88] + Bijectors v0.15.12 [082447d4] + ChainRules v1.72.6 [d360d2e6] + ChainRulesCore v1.26.0 [0ca39b1e] + Chairmarks v1.3.1 [9e997f8a] + ChangesOfVariables v0.1.10 [861a8166] + Combinatorics v1.0.3 [38540f10] + CommonSolve v0.2.4 [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 v0.7.12 [31c24e10] + Distributions v0.25.122 [ced4e74d] + DistributionsAD v0.6.58 [ffbed154] + DocStringExtensions v0.9.5 [366bfd00] + DynamicPPL v0.38.9 [cad2338a] + EllipticalSliceSampling v2.0.0 [4e289a0a] + EnumX v1.0.5 [e2ba6199] + ExprTools v0.1.10 [55351af7] + ExproniconLite v0.10.14 [7a1cc6ca] + FFTW v1.10.0 [9aa1b823] + FastClosures v0.3.2 [1a297f60] + FillArrays v1.15.0 [6a86dc24] + FiniteDiff v2.29.0 [f6369f11] + ForwardDiff v1.3.0 [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 [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 v1.3.0 [ae98c720] + Jieko v0.2.1 [5ab0869b] + KernelDensity v0.6.10 [b964fa9f] + LaTeXStrings v1.4.0 [1d6d02ad] + LeftChildRightSiblingTrees v0.2.1 [6f1fad26] + Libtask v0.9.10 [d3d80556] + LineSearches v7.5.1 [6fdf6af0] + LogDensityProblems v2.2.0 [996a588d] + LogDensityProblemsAD v1.13.1 [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.2.0 [c7f686f2] + MCMCChains v7.6.0 [be115224] + MCMCDiagnosticTools v0.3.15 [e80e1ace] + MLJModelInterface v1.12.0 [1914dd2f] + MacroTools v0.5.16 [dbb5928d] + MappedArrays v0.4.2 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [dbe65cb8] + MistyClosures v2.1.0 [2e0e35c7] + Moshi v0.3.7 [d41bc354] + NLSolversBase v7.10.0 [77ba4419] + NaNMath v1.1.3 [86f7a689] + NamedArrays v0.10.5 [c020b1a1] + NaturalSort v1.0.0 [6fe1bfb0] + OffsetArrays v1.17.0 [429524aa] + Optim v1.13.3 [3bd65402] + Optimisers v0.4.6 [7f7a1694] + Optimization v5.1.0 [bca83a33] + OptimizationBase v4.0.2 [36348300] + OptimizationOptimJL v0.4.8 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.36 [69de0a69] + Parsers v2.8.3 [85a6dd25] + PositiveFactorizations v0.2.4 [d236fae5] + PreallocationTools v0.4.34 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.0 [08abe8d2] + PrettyTables v3.1.2 [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 v3.39.0 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.9.0 [f2b01f46] + Roots v2.2.10 [7e49a35a] + RuntimeGeneratedFunctions v0.5.16 ⌅ [26aad666] + SSMProblems v0.5.2 [0bca4576] + SciMLBase v2.127.0 [a6db7da4] + SciMLLogging v1.5.0 [c0aeaf25] + SciMLOperators v1.13.0 [431bcebd] + SciMLPublic v1.0.0 [53ae85a6] + SciMLStructures v1.7.0 [30f210dd] + ScientificTypesBase v3.0.0 [efcf1570] + Setfield v1.1.2 [a2af1166] + SortingAlgorithms v1.2.2 [9f842d2f] + SparseConnectivityTracer v1.1.3 [dc90abb0] + SparseInverseSubset v0.1.2 [0a514795] + SparseMatrixColorings v0.4.23 [276daf66] + SpecialFunctions v2.6.1 [171d559e] + SplittablesBase v0.1.15 [90137ffa] + StaticArrays v1.9.15 [1e83bf80] + StaticArraysCore v1.4.4 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 [2913bbd2] + StatsBase v0.34.8 [4c63d2b9] + StatsFuns v1.5.2 [892a3eda] + StringManipulation v0.4.2 [09ab397b] + StructArrays v0.7.2 [ec057cc2] + StructUtils v2.6.0 [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 v0.41.4 [efce3f68] + WoodburyMatrices v1.0.0 [700de1a5] + ZygoteRules v0.2.7 [f5851436] + FFTW_jll v3.3.11+0 [1d5cc7b8] + IntelOpenMP_jll v2025.2.0+0 [856f044c] + MKL_jll v2025.2.0+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [1317d2d5] + oneTBB_jll v2022.0.0+1 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v1.0.0 [76f85450] + LibGit2 v1.11.0 [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 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.13.0 [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 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] + LibCURL_jll v8.17.0+0 [e37daf67] + LibGit2_jll v1.9.1+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.11.4 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.4+0 [efcefdf7] + PCRE2_jll v10.47.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.1+2 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.68.0+1 [3f19e933] + p7zip_jll v17.7.0+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 6.34s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Error: Failed to use TestEnv.jl; test dependencies will not be precompiled │ exception = │ UndefVarError: `project_rel_path` not defined in `TestEnv` │ Suggestion: this global was defined as `Pkg.Operations.project_rel_path` but not assigned a value. │ Stacktrace: │ [1] get_test_dir(ctx::Pkg.Types.Context, pkgspec::PackageSpec) │ @ TestEnv ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/common.jl:75 │ [2] test_dir_has_project_file │ @ ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/common.jl:52 [inlined] │ [3] maybe_gen_project_override! │ @ ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/common.jl:83 [inlined] │ [4] activate(pkg::String; allow_reresolve::Bool) │ @ TestEnv ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/activate_set.jl:12 │ [5] activate(pkg::String) │ @ TestEnv ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/activate_set.jl:9 │ [6] top-level scope │ @ /PkgEval.jl/scripts/precompile.jl:24 │ [7] include(mod::Module, _path::String) │ @ Base ./Base.jl:309 │ [8] exec_options(opts::Base.JLOptions) │ @ Base ./client.jl:344 │ [9] _start() │ @ Base ./client.jl:577 └ @ Main /PkgEval.jl/scripts/precompile.jl:26 Precompiling package dependencies... Precompiling packages... 1111.7 ms ✓ Optimisers → OptimisersAdaptExt 3186.1 ms ✓ AbstractMCMC 2325.7 ms ✓ SciMLBase → SciMLBaseChainRulesCoreExt 2905.4 ms ✓ SciMLBase → SciMLBaseForwardDiffExt 6034.3 ms ✓ OptimizationBase 12767.0 ms ✓ Distributions 2276.8 ms ✓ LogDensityProblemsAD → LogDensityProblemsADForwardDiffExt 1591.8 ms ✓ Roots → RootsForwardDiffExt 2479.0 ms ✓ Interpolations → InterpolationsForwardDiffExt 6155.5 ms ✓ Optim 5178.9 ms ✓ AdvancedHMC 3791.5 ms ✓ AbstractPPL 1460.6 ms ✓ OptimizationBase → OptimizationForwardDiffExt 4498.9 ms ✓ Optimization 4536.2 ms ✓ Distributions → DistributionsTestExt 3870.7 ms ✓ Distributions → DistributionsDensityInterfaceExt 4183.8 ms ✓ Distributions → DistributionsChainRulesCoreExt 5888.5 ms ✓ MCMCDiagnosticTools 5209.6 ms ✓ SciMLBase → SciMLBaseDistributionsExt 3940.7 ms ✓ AdvancedHMC → AdvancedHMCADTypesExt 6039.0 ms ✓ AbstractPPL → AbstractPPLDistributionsExt 28740.2 ms ✓ OptimizationOptimJL 5555.7 ms ✓ SSMProblems 6407.8 ms ✓ AdvancedMH 5171.5 ms ✓ EllipticalSliceSampling 4739.5 ms ✓ KernelDensity 6203.8 ms ✓ AdvancedVI 8337.7 ms ✓ Bijectors 9555.1 ms ✓ DistributionsAD 5456.5 ms ✓ AdvancedPS 4589.6 ms ✓ AdvancedMH → AdvancedMHStructArraysExt 4911.9 ms ✓ AdvancedMH → AdvancedMHForwardDiffExt 10453.6 ms ✓ MCMCChains 4119.0 ms ✓ Bijectors → BijectorsForwardDiffExt 18923.8 ms ✓ DynamicPPL 4024.5 ms ✓ AdvancedVI → AdvancedVIBijectorsExt 4373.6 ms ✓ DistributionsAD → DistributionsADForwardDiffExt 4145.2 ms ✓ Bijectors → BijectorsDistributionsADExt 4948.7 ms ✓ AdvancedPS → AdvancedPSLibtaskExt 8671.7 ms ✓ AdvancedHMC → AdvancedHMCMCMCChainsExt 8356.5 ms ✓ AdvancedMH → AdvancedMHMCMCChainsExt 6176.4 ms ✓ DynamicPPL → DynamicPPLChainRulesCoreExt 9966.9 ms ✓ DynamicPPL → DynamicPPLMCMCChainsExt 5981.2 ms ✓ DynamicPPL → DynamicPPLForwardDiffExt 17340.0 ms ✓ Turing 14599.7 ms ✓ Turing → TuringOptimExt 46 dependencies successfully precompiled in 313 seconds. 263 already precompiled. Precompilation completed after 308.58s ################################################################################ # 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_WKRcOw/Project.toml` [4c88cf16] + Aqua v0.8.14 [aaaa29a8] + Clustering v0.15.8 [bbc10e6e] + DynamicHMC v3.5.1 [26cc04aa] + FiniteDifferences v0.12.33 [09f84164] + HypothesisTests v0.11.6 [3e6eede4] + OptimizationBBO v0.4.5 [4e6fcdb7] + OptimizationNLopt v0.3.8 [37e2e3b7] + ReverseDiff v1.16.1 [860ef19b] + StableRNGs v1.0.4 [a759f4b9] + TimerOutputs v0.5.29 [fce5fe82] + Turing v0.41.4 Updating `/tmp/jl_WKRcOw/Manifest.toml` [0bf59076] + AdvancedHMC v0.8.3 [4c88cf16] + Aqua v0.8.14 [a134a8b2] + BlackBoxOptim v0.6.3 [fa961155] + CEnum v0.5.0 [a9c8d775] + CPUTime v1.0.0 [aaaa29a8] + Clustering v0.15.8 [b4f34e82] + Distances v0.10.12 [bbc10e6e] + DynamicHMC v3.5.1 [cad2338a] + EllipticalSliceSampling v2.0.0 [26cc04aa] + FiniteDifferences v0.12.33 [09f84164] + HypothesisTests v0.11.6 ⌅ [682c06a0] ↓ JSON v1.3.0 ⇒ v0.21.4 [1fad7336] + LazyStack v0.1.3 [6f1fad26] + Libtask v0.9.10 [dbe65cb8] + MistyClosures v2.1.0 [76087f3c] + NLopt v1.2.1 [b8a86587] + NearestNeighbors v0.4.24 [3e6eede4] + OptimizationBBO v0.4.5 [4e6fcdb7] + OptimizationNLopt v0.3.8 [65ce6f38] + PackageExtensionCompat v1.0.2 [37e2e3b7] + ReverseDiff v1.16.1 [708f8203] + Richardson v1.4.2 [d4ead438] + SpatialIndexing v0.1.6 [860ef19b] + StableRNGs v1.0.4 [5e0ebb24] + Strided v2.3.2 [4db3bf67] + StridedViews v0.4.1 [ec057cc2] - StructUtils v2.6.0 [02d47bb6] + TensorCast v0.4.9 [a759f4b9] + TimerOutputs v0.5.29 [24ddb15e] + TransmuteDims v0.1.17 [9d95972d] + TupleTools v1.6.0 [fce5fe82] + Turing v0.41.4 [079eb43e] + NLopt_jll v2.10.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Test Successfully re-resolved Status `/tmp/jl_WKRcOw/Project.toml` [47edcb42] ADTypes v1.19.0 [80f14c24] AbstractMCMC v5.10.0 [7a57a42e] AbstractPPL v0.13.6 [5b7e9947] AdvancedMH v0.8.9 [576499cb] AdvancedPS v0.7.0 ⌅ [b5ca4192] AdvancedVI v0.4.1 [4c88cf16] Aqua v0.8.14 [198e06fe] BangBang v0.4.6 [76274a88] Bijectors v0.15.12 [aaaa29a8] Clustering v0.15.8 [861a8166] Combinatorics v1.0.3 [31c24e10] Distributions v0.25.122 [ced4e74d] DistributionsAD v0.6.58 [bbc10e6e] DynamicHMC v3.5.1 [366bfd00] DynamicPPL v0.38.9 [26cc04aa] FiniteDifferences v0.12.33 [f6369f11] ForwardDiff v1.3.0 [09f84164] HypothesisTests v0.11.6 [6fdf6af0] LogDensityProblems v2.2.0 [996a588d] LogDensityProblemsAD v1.13.1 [c7f686f2] MCMCChains v7.6.0 [86f7a689] NamedArrays v0.10.5 [429524aa] Optim v1.13.3 [7f7a1694] Optimization v5.1.0 [3e6eede4] OptimizationBBO v0.4.5 [4e6fcdb7] OptimizationNLopt v0.3.8 [36348300] OptimizationOptimJL v0.4.8 [90014a1f] PDMats v0.11.36 [37e2e3b7] ReverseDiff v1.16.1 [276daf66] SpecialFunctions v2.6.1 [860ef19b] StableRNGs v1.0.4 [2913bbd2] StatsBase v0.34.8 [4c63d2b9] StatsFuns v1.5.2 [a759f4b9] TimerOutputs v0.5.29 [fce5fe82] Turing v0.41.4 [37e2e46d] LinearAlgebra v1.13.0 [44cfe95a] Pkg v1.13.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_WKRcOw/Manifest.toml` [47edcb42] ADTypes v1.19.0 [621f4979] AbstractFFTs v1.5.0 [80f14c24] AbstractMCMC v5.10.0 [7a57a42e] AbstractPPL v0.13.6 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.4.0 [0bf59076] AdvancedHMC v0.8.3 [5b7e9947] AdvancedMH v0.8.9 [576499cb] AdvancedPS v0.7.0 ⌅ [b5ca4192] AdvancedVI v0.4.1 [66dad0bd] AliasTables v1.1.3 [4c88cf16] Aqua v0.8.14 [dce04be8] ArgCheck v2.5.0 [4fba245c] ArrayInterface v7.22.0 [13072b0f] AxisAlgorithms v1.1.0 [39de3d68] AxisArrays v0.4.8 [198e06fe] BangBang v0.4.6 [9718e550] Baselet v0.1.1 [76274a88] Bijectors v0.15.12 [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 v1.0.3 [38540f10] CommonSolve v0.2.4 [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 v0.7.12 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.122 [ced4e74d] DistributionsAD v0.6.58 [ffbed154] DocStringExtensions v0.9.5 [bbc10e6e] DynamicHMC v3.5.1 [366bfd00] DynamicPPL v0.38.9 [cad2338a] EllipticalSliceSampling v2.0.0 [4e289a0a] EnumX v1.0.5 [e2ba6199] ExprTools v0.1.10 [55351af7] ExproniconLite v0.10.14 [7a1cc6ca] FFTW v1.10.0 [9aa1b823] FastClosures v0.3.2 [1a297f60] FillArrays v1.15.0 [6a86dc24] FiniteDiff v2.29.0 [26cc04aa] FiniteDifferences v0.12.33 [f6369f11] ForwardDiff v1.3.0 [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 [09f84164] HypothesisTests v0.11.6 [22cec73e] InitialValues v0.3.1 [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 v0.6.10 [b964fa9f] LaTeXStrings v1.4.0 [1fad7336] LazyStack v0.1.3 [1d6d02ad] LeftChildRightSiblingTrees v0.2.1 [6f1fad26] Libtask v0.9.10 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[6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.13.0 [f489334b] StyledStrings v1.13.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] LibCURL_jll v8.17.0+0 [e37daf67] LibGit2_jll v1.9.1+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.11.4 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.4+0 [efcefdf7] PCRE2_jll v10.47.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.1+2 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.68.0+1 [3f19e933] p7zip_jll v17.7.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... [ Info: [Turing]: progress logging is disabled globally [ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[s] [ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[m] [ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[s] [ Info: Running AD on demo_assume_index_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[m] [ Info: Running AD on demo_assume_index_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[s] [ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[m] [ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[s] [ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[m] [ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[s] [ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[m] [ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[s] [ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[m] [ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoForwardDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[s] [ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[m] [ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[s] [ Info: Running AD on demo_assume_index_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[m] [ Info: Running AD on demo_assume_index_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528044, 0.11182461275017186]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[s] [ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[m] [ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[s] [ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[m] [ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[s] [ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[m] [ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 1.8770958195241771] actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354]) [ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[s] [ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[m] [ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[s] [ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[m] [ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[s] [ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) [ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[m] [ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoReverseDiff() params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665] actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175]) expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186]) constructor: Error During Test at /home/pkgeval/.julia/packages/Turing/EThRU/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}}}}, 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] #TapedTask#67 @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:54 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:53 [inlined] [9] AdvancedPS.LibtaskModel(::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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] 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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:454 [12] top-level scope @ ~/.julia/packages/Turing/EThRU/test/essential/container.jl:10 [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [14] macro expansion @ ~/.julia/packages/Turing/EThRU/test/essential/container.jl:21 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [16] macro expansion @ ~/.julia/packages/Turing/EThRU/test/essential/container.jl:25 [inlined] [17] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [18] top-level scope @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:33 [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [20] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:42 [inlined] [21] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [22] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:25 [inlined] [23] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [24] top-level scope @ none:6 [25] eval(m::Module, e::Any) @ Core ./boot.jl:489 [26] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [27] _start() @ Base ./client.jl:577 fork: Error During Test at /home/pkgeval/.julia/packages/Turing/EThRU/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}}}}, 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] #TapedTask#67 @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:54 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:53 [inlined] [9] AdvancedPS.LibtaskModel(::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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] 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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:454 [12] top-level scope @ ~/.julia/packages/Turing/EThRU/test/essential/container.jl:10 [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [14] macro expansion @ ~/.julia/packages/Turing/EThRU/test/essential/container.jl:39 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [16] macro expansion @ ~/.julia/packages/Turing/EThRU/test/essential/container.jl:51 [inlined] [17] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [18] top-level scope @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:33 [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [20] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:42 [inlined] [21] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [22] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:25 [inlined] [23] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [24] top-level scope @ none:6 [25] eval(m::Module, e::Any) @ Core ./boot.jl:489 [26] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [27] _start() @ Base ./client.jl:577 ┌ 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/mcqES/src/sample.jl:432 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ Info: Found initial step size └ ϵ = 1.6 ┌ Warning: Only a single process available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:620 ┌ Info: Found initial step size └ ϵ = 3.2 ┌ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 ┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 [ 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/mcqES/src/sample.jl:432 ┌ Warning: Number of chains (10) is greater than number of samples per chain (1) └ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437 models: Error During Test at /home/pkgeval/.julia/packages/Turing/EThRU/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}}}}, 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] #TapedTask#67 @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:54 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:53 [inlined] [9] AdvancedPS.LibtaskModel(::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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] 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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:454 [12] (::Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}}}) @ Base ./array.jl:828 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:147 [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:134 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{nparticles::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:180 [18] step @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:162 [inlined] [19] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined] [20] (::AbstractMCMC.var"#27#28"{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}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134 [21] with_logstate(f::AbstractMCMC.var"#27#28"{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}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [22] 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:651 [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157 [24] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined] [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/mcqES/src/sample.jl:168 [26] 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}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123 [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:121 [28] sample @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:108 [inlined] [29] #sample#1 @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:71 [inlined] [30] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:68 [31] top-level scope @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:13 [32] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [33] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:28 [inlined] [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:36 [inlined] [36] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [37] top-level scope @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:33 [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:46 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:25 [inlined] [42] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [43] top-level scope @ none:6 [44] eval(m::Module, e::Any) @ Core ./boot.jl:489 [45] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [46] _start() @ Base ./client.jl:577 chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/EThRU/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}}}}, 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] #TapedTask#67 @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:54 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:53 [inlined] [9] AdvancedPS.LibtaskModel(::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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] 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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:454 [12] (::Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}}}) @ Base ./array.jl:828 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:147 [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, 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}, 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/EThRU/src/mcmc/particle_mcmc.jl:134 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{nparticles::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:180 [18] step @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:162 [inlined] [19] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined] [20] (::AbstractMCMC.var"#27#28"{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}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134 [21] with_logstate(f::AbstractMCMC.var"#27#28"{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}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [22] 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:651 [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157 [24] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined] [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/mcqES/src/sample.jl:168 [26] 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}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123 [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:121 [28] sample @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:108 [inlined] [29] #sample#1 @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:71 [inlined] [30] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:68 [31] test_chain_logp_metadata(spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/EThRU/test/test_utils/sampler.jl:22 [32] top-level scope @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:13 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:54 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:54 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [38] top-level scope @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/EThRU/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:577 logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/EThRU/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}}}}, 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] #TapedTask#67 @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:54 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:53 [inlined] [9] AdvancedPS.LibtaskModel(::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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] 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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:454 [12] (::Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}}}) @ Base ./array.jl:828 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:147 [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:134 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{nparticles::Int64}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:180 [18] step @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:162 [inlined] [19] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined] [20] (::AbstractMCMC.var"#27#28"{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}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134 [21] with_logstate(f::AbstractMCMC.var"#27#28"{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}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [22] 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:651 [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157 [24] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined] [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/mcqES/src/sample.jl:168 [26] 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}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123 [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:121 [28] sample @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:108 [inlined] [29] #sample#1 @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:71 [inlined] [30] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:68 [31] top-level scope @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:13 [32] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [33] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:58 [inlined] [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:70 [inlined] [36] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [37] top-level scope @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:33 [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:46 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:25 [inlined] [42] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [43] top-level scope @ none:6 [44] eval(m::Module, e::Any) @ Core ./boot.jl:489 [45] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [46] _start() @ Base ./client.jl:577 chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:99 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}}}}, 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] #TapedTask#67 @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:54 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:53 [inlined] [9] AdvancedPS.LibtaskModel(::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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] 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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:454 [12] (::Turing.Inference.var"#77#78"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#77#78"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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}}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:264 [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:257 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:180 [18] step @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:162 [inlined] [19] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined] [20] (::AbstractMCMC.var"#27#28"{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}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134 [21] with_logstate(f::AbstractMCMC.var"#27#28"{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}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [22] 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:651 [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157 [24] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined] [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/mcqES/src/sample.jl:168 [26] 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}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123 [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/abstractmcmc.jl:85 [28] sample @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:74 [inlined] [29] #sample#1 @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:71 [inlined] [30] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:68 [31] test_chain_logp_metadata(spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/EThRU/test/test_utils/sampler.jl:22 [32] top-level scope @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:81 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:100 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:100 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [38] top-level scope @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/EThRU/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:577 logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:103 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}}}}, 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] #TapedTask#67 @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:54 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:53 [inlined] [9] AdvancedPS.LibtaskModel(::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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] 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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:454 [12] (::Turing.Inference.var"#77#78"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#77#78"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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}}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:264 [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:257 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:180 [18] step @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:162 [inlined] [19] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined] [20] (::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134 [21] with_logstate(f::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [22] 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:651 [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157 [24] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined] [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/mcqES/src/sample.jl:168 [26] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123 [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/abstractmcmc.jl:85 [28] sample @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:74 [inlined] [29] #sample#1 @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:71 [inlined] [30] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:68 [31] top-level scope @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:81 [32] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [33] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:104 [inlined] [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:116 [inlined] [36] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [37] top-level scope @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:33 [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:46 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:25 [inlined] [42] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [43] top-level scope @ none:6 [44] eval(m::Module, e::Any) @ Core ./boot.jl:489 [45] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [46] _start() @ Base ./client.jl:577 reference particle: Error During Test at /home/pkgeval/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:124 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}}}}, 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] #TapedTask#67 @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:54 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:53 [inlined] [9] AdvancedPS.LibtaskModel(::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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] 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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:454 [12] (::Turing.Inference.var"#77#78"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#77#78"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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}}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:264 [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:257 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:180 [18] step @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:162 [inlined] [19] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined] [20] (::AbstractMCMC.var"#27#28"{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}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134 [21] with_logstate(f::AbstractMCMC.var"#27#28"{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}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [22] 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:651 [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157 [24] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined] [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/mcqES/src/sample.jl:168 [26] 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}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123 [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/abstractmcmc.jl:85 [28] sample @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:74 [inlined] [29] #sample#1 @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:71 [inlined] [30] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:68 [31] top-level scope @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:81 [32] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [33] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:125 [inlined] [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:125 [inlined] [36] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [37] top-level scope @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:33 [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:46 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:25 [inlined] [42] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [43] top-level scope @ none:6 [44] eval(m::Module, e::Any) @ Core ./boot.jl:489 [45] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [46] _start() @ Base ./client.jl:577 addlogprob leads to reweighting: Error During Test at /home/pkgeval/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:130 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}}}}, 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] #TapedTask#67 @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:54 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:53 [inlined] [9] AdvancedPS.LibtaskModel(::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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] 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}}}}, 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}}}}, 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}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:454 [12] (::Turing.Inference.var"#77#78"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#77#78"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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 [15] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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}}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:264 [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/particle_mcmc.jl:257 [17] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:180 [18] step @ ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:162 [inlined] [19] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined] [20] (::AbstractMCMC.var"#27#28"{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}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134 [21] with_logstate(f::AbstractMCMC.var"#27#28"{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}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [22] 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:651 [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157 [24] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined] [25] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/mcqES/src/sample.jl:168 [26] 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}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123 [27] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/abstractmcmc.jl:85 [28] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:74 [29] top-level scope @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:81 [30] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [31] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:133 [inlined] [32] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [33] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/particle_mcmc.jl:143 [inlined] [34] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [35] top-level scope @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:33 [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:46 [inlined] [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:25 [inlined] [40] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [41] top-level scope @ none:6 [42] eval(m::Module, e::Any) @ Core ./boot.jl:489 [43] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [44] _start() @ Base ./client.jl:577 ┌ Warning: The model does not contain any parameters. └ @ DynamicPPL.DebugUtils ~/.julia/packages/DynamicPPL/Ut5Ls/src/debug_utils.jl:304 [ 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 [ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 2.0765449812082557) [ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.1697615669122199) [ 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/EThRU/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}}}}}, 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] #TapedTask#67 @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:54 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:53 [inlined] [9] AdvancedPS.LibtaskModel(::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}}}}}, 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}}}}}, 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}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] 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}}}}}, 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}}}}}, 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}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] 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}}, 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/EThRU/src/mcmc/particle_mcmc.jl:454 [12] (::Turing.Inference.var"#77#78"{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}}, 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 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#77#78"{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}}, 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 [15] 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}}, 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}}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:264 [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::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}}, 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/EThRU/src/mcmc/particle_mcmc.jl:257 [17] 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}}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:180 [18] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(AbstractMCMC.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}}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:162 [19] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), 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{}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:396 [20] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/gibbs.jl:373 [21] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, 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, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:328 [22] step @ ~/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:317 [inlined] [23] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined] [24] (::AbstractMCMC.var"#27#28"{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}, 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, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134 [25] with_logstate(f::AbstractMCMC.var"#27#28"{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}, 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, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [26] 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:651 [27] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157 [28] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined] [29] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, 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/mcqES/src/sample.jl:168 [30] 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}, 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/mcqES/src/sample.jl:123 [31] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/abstractmcmc.jl:85 [32] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/abstractmcmc.jl:74 [33] top-level scope @ ~/.julia/packages/Turing/EThRU/test/mcmc/ess.jl:14 [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/ess.jl:48 [inlined] [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/ess.jl:62 [inlined] [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/ess.jl:63 [inlined] [40] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [41] top-level scope @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:33 [42] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [43] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:46 [inlined] [44] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [45] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:25 [inlined] [46] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [47] top-level scope @ none:6 [48] eval(m::Module, e::Any) @ Core ./boot.jl:489 [49] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [50] _start() @ Base ./client.jl:577 MoGtest_default with CSMC + ESS: Error During Test at /home/pkgeval/.julia/packages/Turing/EThRU/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}}}}}, 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] #TapedTask#67 @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:54 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:53 [inlined] [9] AdvancedPS.LibtaskModel(::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}}}}}, 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}}}}}, 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}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38 [10] 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}}}}}, 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}}}}}, 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}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}) @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84 [11] 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}}, 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/EThRU/src/mcmc/particle_mcmc.jl:454 [12] (::Turing.Inference.var"#77#78"{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}}, 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 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#77#78"{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}}, 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 [15] 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}}, 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}}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/particle_mcmc.jl:264 [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::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}}, 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/EThRU/src/mcmc/particle_mcmc.jl:257 [17] 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}}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:180 [18] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(AbstractMCMC.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}}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:162 [19] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), 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{}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:396 [20] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/gibbs.jl:373 [21] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, 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, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:328 [22] step @ ~/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:317 [inlined] [23] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined] [24] (::AbstractMCMC.var"#27#28"{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}, 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, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134 [25] with_logstate(f::AbstractMCMC.var"#27#28"{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}, 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, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [26] 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:651 [27] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157 [28] macro expansion @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined] [29] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, 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/mcqES/src/sample.jl:168 [30] 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}, 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/mcqES/src/sample.jl:123 [31] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/abstractmcmc.jl:85 [32] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, 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/EThRU/src/mcmc/abstractmcmc.jl:74 [33] top-level scope @ ~/.julia/packages/Turing/EThRU/test/mcmc/ess.jl:14 [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/ess.jl:48 [inlined] [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/ess.jl:68 [inlined] [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/Turing/EThRU/test/mcmc/ess.jl:73 [inlined] [40] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [41] top-level scope @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:33 [42] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [43] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:46 [inlined] [44] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [45] macro expansion @ ~/.julia/packages/Turing/EThRU/test/runtests.jl:25 [inlined] [46] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [47] top-level scope @ none:6 [48] eval(m::Module, e::Any) @ Core ./boot.jl:489 [49] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [50] _start() @ Base ./client.jl:577 ┌ 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/EThRU/test/mcmc/gibbs.jl:38 overwritten at /home/pkgeval/.julia/packages/Turing/EThRU/test/mcmc/gibbs.jl:196. WARNING: Method definition (::Type{GibbsTests.Wrapper{T<:Real}})(Any) in module GibbsTests at /home/pkgeval/.julia/packages/Turing/EThRU/test/mcmc/gibbs.jl:38 overwritten at /home/pkgeval/.julia/packages/Turing/EThRU/test/mcmc/gibbs.jl:196. ====================================================================================== 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 197 running 1 of 1 signal (10): User defined signal 1 _ZN4llvm22ConstantFoldTerminatorEPNS_10BasicBlockEbPKNS_17TargetLibraryInfoEPNS_14DomTreeUpdaterE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZL15markAliveBlocksRN4llvm8FunctionERNS_15SmallPtrSetImplIPNS_10BasicBlockEEEPNS_14DomTreeUpdaterE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm23removeUnreachableBlocksERNS_8FunctionEPNS_14DomTreeUpdaterEPNS_16MemorySSAUpdaterE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) _ZN4llvm13GlobalOptPass3runERNS_6ModuleERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) run at /source/usr/include/llvm/IR/PassManagerInternal.h:91 _ZN4llvm11PassManagerINS_6ModuleENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) run at /source/src/pipeline.cpp:787 operator() at /source/src/jitlayers.cpp:1508 withModuleDo<(anonymous namespace)::sizedOptimizerT::operator()(llvm::orc::ThreadSafeModule) [with long unsigned int N = 4]:: > at /source/usr/include/llvm/ExecutionEngine/Orc/ThreadSafeModule.h:136 [inlined] operator() at /source/src/jitlayers.cpp:1469 [inlined] operator() at /source/src/jitlayers.cpp:1644 [inlined] addModule at /source/src/jitlayers.cpp:2101 jl_compile_codeinst_now at /source/src/jitlayers.cpp:682 jl_compile_codeinst_impl at /source/src/jitlayers.cpp:876 jl_compile_method_internal at /source/src/gf.c:3648 _jl_invoke at /source/src/gf.c:4108 [inlined] ijl_apply_generic at /source/src/gf.c:4313 #step#5 at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:177 step at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:162 [inlined] step at /home/pkgeval/.julia/packages/Turing/EThRU/test/mcmc/gibbs.jl:181 step at /home/pkgeval/.julia/packages/Turing/EThRU/test/mcmc/gibbs.jl:173 _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 #gibbs_initialstep_recursive#85 at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:396 unknown function (ip: 0x72f2bcfd05e6) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:373 gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:373 unknown function (ip: 0x72f2bcfb9978) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 #step#83 at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:328 step at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:317 [inlined] macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined] #27 at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134 with_logstate at ./logging/logging.jl:540 unknown function (ip: 0x72f2bcf6903c) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 with_logger at ./logging/logging.jl:651 with_progresslogger at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157 unknown function (ip: 0x72f2cbffd4fe) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined] #mcmcsample#25 at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168 mcmcsample at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123 unknown function (ip: 0x72f2bd16066b) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 #sample#2 at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:85 sample at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:74 [inlined] #sample#1 at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:71 [inlined] sample at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:68 unknown function (ip: 0x72f2bd13f086) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 jl_apply at /source/src/julia.h:2284 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_body at /source/src/interpreter.c:581 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 jl_interpret_toplevel_thunk at /source/src/interpreter.c:884 jl_toplevel_eval_flex at /source/src/toplevel.c:742 jl_eval_toplevel_stmts at /source/src/toplevel.c:585 jl_eval_module_expr at /source/src/toplevel.c:248 [inlined] jl_toplevel_eval_flex at /source/src/toplevel.c:650 jl_eval_toplevel_stmts at /source/src/toplevel.c:585 jl_toplevel_eval_flex at /source/src/toplevel.c:683 ijl_toplevel_eval at /source/src/toplevel.c:754 ijl_toplevel_eval_in at /source/src/toplevel.c:799 eval at ./boot.jl:489 include_string at ./loading.jl:3003 _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 _include at ./loading.jl:3063 include at ./Base.jl:310 IncludeInto at ./Base.jl:311 unknown function (ip: 0x72f2bf2b1052) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 jl_apply at /source/src/julia.h:2284 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_stmt_value at /source/src/interpreter.c:194 [inlined] eval_body at /source/src/interpreter.c:679 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 jl_interpret_toplevel_thunk at /source/src/interpreter.c:884 jl_toplevel_eval_flex at /source/src/toplevel.c:742 jl_eval_toplevel_stmts at /source/src/toplevel.c:585 jl_toplevel_eval_flex at /source/src/toplevel.c:683 ijl_toplevel_eval at /source/src/toplevel.c:754 ijl_toplevel_eval_in at /source/src/toplevel.c:799 eval at ./boot.jl:489 include_string at ./loading.jl:3003 _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 _include at ./loading.jl:3063 include at ./Base.jl:310 IncludeInto at ./Base.jl:311 jfptr_IncludeInto_54631.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 jl_apply at /source/src/julia.h:2284 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_stmt_value at /source/src/interpreter.c:194 [inlined] eval_body at /source/src/interpreter.c:679 jl_interpret_toplevel_thunk at /source/src/interpreter.c:884 jl_toplevel_eval_flex at /source/src/toplevel.c:742 jl_eval_toplevel_stmts at /source/src/toplevel.c:585 jl_toplevel_eval_flex at /source/src/toplevel.c:683 ijl_toplevel_eval at /source/src/toplevel.c:754 ijl_toplevel_eval_in at /source/src/toplevel.c:799 eval at ./boot.jl:489 exec_options at ./client.jl:310 _start at ./client.jl:577 jfptr__start_65414.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 jl_apply at /source/src/julia.h:2284 [inlined] true_main at /source/src/jlapi.c:971 jl_repl_entrypoint at /source/src/jlapi.c:1138 main at /source/cli/loader_exe.c:58 unknown function (ip: 0x72f2f42d4249) at /lib/x86_64-linux-gnu/libc.so.6 __libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S 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_38735.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 jl_apply at /source/src/julia.h:2284 [inlined] start_task at /source/src/task.c:1272 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 0x00007ed1572d53c0 Total snapshots: 402. Utilization: 0% ╎402 @Base/task.jl:1168 wait_forever() 401╎ 402 @Base/task.jl:1246 wait() [1] signal 15: Terminated in expression starting at /PkgEval.jl/scripts/evaluate.jl:210 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_38735.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 jl_apply at /source/src/julia.h:2284 [inlined] start_task at /source/src/task.c:1272 unknown function (ip: (nil)) at (unknown file) Allocations: 27059202 (Pool: 27058501; Big: 701); GC: 22 [197] signal 15: Terminated in expression starting at /home/pkgeval/.julia/packages/Turing/EThRU/test/mcmc/gibbs.jl:138 jl_genericmemory_copyto at /source/src/genericmemory.c:258 unsafe_copyto! at ./genericmemory.jl:143 [inlined] _growend_internal! at ./array.jl:1185 _growend! at ./array.jl:1205 [inlined] resize! at ./array.jl:1540 resize! at ./../usr/share/julia/Compiler/src/ssair/ir.jl:293 resize! at ./../usr/share/julia/Compiler/src/ssair/ir.jl:1735 [inlined] batch_inline! at ./../usr/share/julia/Compiler/src/ssair/inlining.jl:646 ssa_inlining_pass! at ./../usr/share/julia/Compiler/src/ssair/inlining.jl:79 [inlined] run_passes_ipo_safe at ./../usr/share/julia/Compiler/src/optimize.jl:1035 run_passes_ipo_safe at ./../usr/share/julia/Compiler/src/optimize.jl:1048 [inlined] optimize at ./../usr/share/julia/Compiler/src/optimize.jl:1021 jfptr_optimize_88466.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 finish_nocycle at ./../usr/share/julia/Compiler/src/typeinfer.jl:278 jfptr_finish_nocycle_86957.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 typeinf at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4508 typeinf_ext at ./../usr/share/julia/Compiler/src/typeinfer.jl:1527 typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1704 [inlined] typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1713 jfptr_typeinf_ext_toplevel_87872.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 jl_apply at /source/src/julia.h:2284 [inlined] jl_type_infer at /source/src/gf.c:463 jl_compile_method_internal at /source/src/gf.c:3636 _jl_invoke at /source/src/gf.c:4108 [inlined] ijl_apply_generic at /source/src/gf.c:4313 gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:373 unknown function (ip: 0x72f2bd0258ad) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 #gibbs_initialstep_recursive#85 at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:413 unknown function (ip: 0x72f2bcfd05e6) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:373 gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:373 unknown function (ip: 0x72f2bcfb9978) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 #step#83 at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:328 step at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/gibbs.jl:317 [inlined] macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined] #27 at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134 with_logstate at ./logging/logging.jl:540 unknown function (ip: 0x72f2bcf6903c) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 with_logger at ./logging/logging.jl:651 with_progresslogger at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157 unknown function (ip: 0x72f2cbffd4fe) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined] #mcmcsample#25 at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168 mcmcsample at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123 unknown function (ip: 0x72f2bd16066b) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 #sample#2 at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:85 sample at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:74 [inlined] #sample#1 at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:71 [inlined] sample at /home/pkgeval/.julia/packages/Turing/EThRU/src/mcmc/abstractmcmc.jl:68 unknown function (ip: 0x72f2bd13f086) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 jl_apply at /source/src/julia.h:2284 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_body at /source/src/interpreter.c:581 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 jl_interpret_toplevel_thunk at /source/src/interpreter.c:884 jl_toplevel_eval_flex at /source/src/toplevel.c:742 jl_eval_toplevel_stmts at /source/src/toplevel.c:585 jl_eval_module_expr at /source/src/toplevel.c:248 [inlined] jl_toplevel_eval_flex at /source/src/toplevel.c:650 jl_eval_toplevel_stmts at /source/src/toplevel.c:585 jl_toplevel_eval_flex at /source/src/toplevel.c:683 ijl_toplevel_eval at /source/src/toplevel.c:754 ijl_toplevel_eval_in at /source/src/toplevel.c:799 eval at ./boot.jl:489 include_string at ./loading.jl:3003 _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 _include at ./loading.jl:3063 include at ./Base.jl:310 IncludeInto at ./Base.jl:311 unknown function (ip: 0x72f2bf2b1052) at (unknown file) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 jl_apply at /source/src/julia.h:2284 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_stmt_value at /source/src/interpreter.c:194 [inlined] eval_body at /source/src/interpreter.c:679 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:550 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 eval_body at /source/src/interpreter.c:558 jl_interpret_toplevel_thunk at /source/src/interpreter.c:884 jl_toplevel_eval_flex at /source/src/toplevel.c:742 jl_eval_toplevel_stmts at /source/src/toplevel.c:585 jl_toplevel_eval_flex at /source/src/toplevel.c:683 ijl_toplevel_eval at /source/src/toplevel.c:754 ijl_toplevel_eval_in at /source/src/toplevel.c:799 eval at ./boot.jl:489 include_string at ./loading.jl:3003 _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 _include at ./loading.jl:3063 include at ./Base.jl:310 IncludeInto at ./Base.jl:311 jfptr_IncludeInto_54631.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 jl_apply at /source/src/julia.h:2284 [inlined] do_call at /source/src/interpreter.c:123 eval_value at /source/src/interpreter.c:243 eval_stmt_value at /source/src/interpreter.c:194 [inlined] eval_body at /source/src/interpreter.c:679 jl_interpret_toplevel_thunk at /source/src/interpreter.c:884 jl_toplevel_eval_flex at /source/src/toplevel.c:742 jl_eval_toplevel_stmts at /source/src/toplevel.c:585 jl_toplevel_eval_flex at /source/src/toplevel.c:683 ijl_toplevel_eval at /source/src/toplevel.c:754 ijl_toplevel_eval_in at /source/src/toplevel.c:799 eval at ./boot.jl:489 exec_options at ./client.jl:310 _start at ./client.jl:577 jfptr__start_65414.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4116 [inlined] ijl_apply_generic at /source/src/gf.c:4313 jl_apply at /source/src/julia.h:2284 [inlined] true_main at /source/src/jlapi.c:971 jl_repl_entrypoint at /source/src/jlapi.c:1138 main at /source/cli/loader_exe.c:58 unknown function (ip: 0x72f2f42d4249) at /lib/x86_64-linux-gnu/libc.so.6 __libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S unknown function (ip: (nil)) at (unknown file) Allocations: 1149855997 (Pool: 1149849008; Big: 6989); GC: 205 PkgEval terminated after 2728.02s: test duration exceeded the time limit