Package evaluation of Turing on Julia 1.13.0-DEV.985 (9c94e7ae32*) started at 2025-08-15T03:14:07.586 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.69s ################################################################################ # Installation # Installing Turing... Resolving package versions... Updating `~/.julia/environments/v1.13/Project.toml` [fce5fe82] + Turing v0.40.1 Updating `~/.julia/environments/v1.13/Manifest.toml` [47edcb42] + ADTypes v1.17.0 [621f4979] + AbstractFFTs v1.5.0 [80f14c24] + AbstractMCMC v5.7.2 [7a57a42e] + AbstractPPL v0.13.0 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.3.0 [0bf59076] + AdvancedHMC v0.8.1 [5b7e9947] + AdvancedMH v0.8.8 [576499cb] + AdvancedPS v0.7.0 [b5ca4192] + AdvancedVI v0.4.1 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [4fba245c] + ArrayInterface v7.19.0 [13072b0f] + AxisAlgorithms v1.1.0 [39de3d68] + AxisArrays v0.4.7 [198e06fe] + BangBang v0.4.4 [9718e550] + Baselet v0.1.1 [76274a88] + Bijectors v0.15.9 [082447d4] + ChainRules v1.72.5 [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 v4.18.0 [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 v0.18.22 [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 v0.7.4 [31c24e10] + Distributions v0.25.120 [ced4e74d] + DistributionsAD v0.6.58 [ffbed154] + DocStringExtensions v0.9.5 [366bfd00] + DynamicPPL v0.37.1 [cad2338a] + EllipticalSliceSampling v2.0.0 [4e289a0a] + EnumX v1.0.5 [e2ba6199] + ExprTools v0.1.10 [55351af7] + ExproniconLite v0.10.14 [7a1cc6ca] + FFTW v1.9.0 [9aa1b823] + FastClosures v0.3.2 [1a297f60] + FillArrays v1.13.0 [6a86dc24] + FiniteDiff v2.28.0 [f6369f11] + ForwardDiff v1.0.1 [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 v0.16.1 [8197267c] + IntervalSets v0.7.11 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.4 [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 [5be7bae1] + LBFGSB v0.4.1 [b964fa9f] + LaTeXStrings v1.4.0 [1d6d02ad] + LeftChildRightSiblingTrees v0.2.1 [6f1fad26] + Libtask v0.9.4 [d3d80556] + LineSearches v7.4.0 [6fdf6af0] + LogDensityProblems v2.1.2 [996a588d] + LogDensityProblemsAD v1.13.1 [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.1.0 [c7f686f2] + MCMCChains v7.1.0 [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 v0.10.4 [c020b1a1] + NaturalSort v1.0.0 [6fe1bfb0] + OffsetArrays v1.17.0 [429524aa] + Optim v1.13.2 [3bd65402] + Optimisers v0.4.6 [7f7a1694] + Optimization v4.5.0 [bca83a33] + OptimizationBase v2.10.0 [36348300] + OptimizationOptimJL v0.4.3 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.35 [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.3 [85a6dd25] + PositiveFactorizations v0.2.4 [aea7be01] + PrecompileTools v1.3.2 [21216c6a] + Preferences v1.5.0 [08abe8d2] + PrettyTables v2.4.0 [33c8b6b6] + ProgressLogging v0.1.5 [92933f4c] + ProgressMeter v1.10.4 [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 v3.37.0 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.8.0 [f2b01f46] + Roots v2.2.8 [7e49a35a] + RuntimeGeneratedFunctions v0.5.15 [26aad666] + SSMProblems v0.5.1 [0bca4576] + SciMLBase v2.110.0 [c0aeaf25] + SciMLOperators v1.5.0 [53ae85a6] + SciMLStructures v1.7.0 [30f210dd] + ScientificTypesBase v3.0.0 [efcf1570] + Setfield v1.1.2 [a2af1166] + SortingAlgorithms v1.2.2 [9f842d2f] + SparseConnectivityTracer v1.0.0 [dc90abb0] + SparseInverseSubset v0.1.2 [0a514795] + SparseMatrixColorings v0.4.21 [276daf66] + SpecialFunctions v2.5.1 [171d559e] + SplittablesBase v0.1.15 [90137ffa] + StaticArrays v1.9.14 [1e83bf80] + StaticArraysCore v1.4.3 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 [2913bbd2] + StatsBase v0.34.6 [4c63d2b9] + StatsFuns v1.5.0 [892a3eda] + StringManipulation v0.4.1 [09ab397b] + StructArrays v0.7.1 [2efcf032] + SymbolicIndexingInterface v0.3.43 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [5d786b92] + TerminalLoggers v0.1.7 [28d57a85] + Transducers v0.4.84 [fce5fe82] + Turing v0.40.1 [3a884ed6] + UnPack v1.0.2 [efce3f68] + WoodburyMatrices v1.0.0 [700de1a5] + ZygoteRules v0.2.7 [f5851436] + FFTW_jll v3.3.11+0 [1d5cc7b8] + IntelOpenMP_jll v2025.2.0+0 [81d17ec3] + L_BFGS_B_jll v3.0.1+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+0 [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 v0.6.4 [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 v0.7.0 [9e88b42a] + Serialization v1.11.0 [1a1011a3] + SharedArrays v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.11.0 [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 v8.15.0+1 [e37daf67] + LibGit2_jll v1.9.1+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.7.15 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.5+0 [458c3c95] + OpenSSL_jll v3.5.2+0 [efcefdf7] + PCRE2_jll v10.45.0+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 v5.13.1+0 [8e850ede] + nghttp2_jll v1.65.0+0 [3f19e933] + p7zip_jll v17.5.0+2 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 6.24s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 821.84s ################################################################################ # Testing # Testing Turing Status `/tmp/jl_35FfzK/Project.toml` [47edcb42] ADTypes v1.17.0 [80f14c24] AbstractMCMC v5.7.2 [7a57a42e] AbstractPPL v0.13.0 [5b7e9947] AdvancedMH v0.8.8 [576499cb] AdvancedPS v0.7.0 [b5ca4192] AdvancedVI v0.4.1 [4c88cf16] Aqua v0.8.14 [198e06fe] BangBang v0.4.4 [76274a88] Bijectors v0.15.9 [aaaa29a8] Clustering v0.15.8 [861a8166] Combinatorics v1.0.3 [31c24e10] Distributions v0.25.120 [ced4e74d] DistributionsAD v0.6.58 [bbc10e6e] DynamicHMC v3.5.1 [366bfd00] DynamicPPL v0.37.1 [26cc04aa] FiniteDifferences v0.12.32 [f6369f11] ForwardDiff v1.0.1 [09f84164] HypothesisTests v0.11.5 [6fdf6af0] LogDensityProblems v2.1.2 [996a588d] LogDensityProblemsAD v1.13.1 [c7f686f2] MCMCChains v7.1.0 [86f7a689] NamedArrays v0.10.4 [429524aa] Optim v1.13.2 [7f7a1694] Optimization v4.5.0 [3e6eede4] OptimizationBBO v0.4.1 [4e6fcdb7] OptimizationNLopt v0.3.2 [36348300] OptimizationOptimJL v0.4.3 [90014a1f] PDMats v0.11.35 [37e2e3b7] ReverseDiff v1.16.1 [276daf66] SpecialFunctions v2.5.1 [860ef19b] StableRNGs v1.0.3 [2913bbd2] StatsBase v0.34.6 [4c63d2b9] StatsFuns v1.5.0 [a759f4b9] TimerOutputs v0.5.29 [fce5fe82] Turing v0.40.1 [37e2e46d] LinearAlgebra v1.13.0 [44cfe95a] Pkg v1.13.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_35FfzK/Manifest.toml` [47edcb42] ADTypes v1.17.0 [621f4979] AbstractFFTs v1.5.0 [80f14c24] AbstractMCMC v5.7.2 [7a57a42e] AbstractPPL v0.13.0 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.3.0 [0bf59076] AdvancedHMC v0.8.1 [5b7e9947] AdvancedMH v0.8.8 [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 v7.19.0 [13072b0f] AxisAlgorithms v1.1.0 [39de3d68] AxisArrays v0.4.7 [198e06fe] BangBang v0.4.4 [9718e550] Baselet v0.1.1 [76274a88] Bijectors v0.15.9 [a134a8b2] BlackBoxOptim v0.6.3 [fa961155] CEnum v0.5.0 [a9c8d775] CPUTime v1.0.0 [082447d4] ChainRules v1.72.5 [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 v4.18.0 [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 v0.18.22 [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 v0.7.4 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.120 [ced4e74d] DistributionsAD v0.6.58 [ffbed154] DocStringExtensions v0.9.5 [bbc10e6e] DynamicHMC v3.5.1 [366bfd00] DynamicPPL v0.37.1 [cad2338a] EllipticalSliceSampling v2.0.0 [4e289a0a] EnumX v1.0.5 [e2ba6199] ExprTools v0.1.10 [55351af7] ExproniconLite v0.10.14 [7a1cc6ca] FFTW v1.9.0 [9aa1b823] FastClosures v0.3.2 [1a297f60] FillArrays v1.13.0 [6a86dc24] FiniteDiff v2.28.0 [26cc04aa] FiniteDifferences v0.12.32 [f6369f11] ForwardDiff v1.0.1 [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 v0.11.5 [22cec73e] InitialValues v0.3.1 [a98d9a8b] Interpolations v0.16.1 [8197267c] IntervalSets v0.7.11 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.4 [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 [5be7bae1] LBFGSB v0.4.1 [b964fa9f] LaTeXStrings v1.4.0 [1fad7336] LazyStack v0.1.3 [1d6d02ad] LeftChildRightSiblingTrees v0.2.1 [6f1fad26] Libtask v0.9.4 [d3d80556] LineSearches v7.4.0 [6fdf6af0] LogDensityProblems v2.1.2 [996a588d] LogDensityProblemsAD v1.13.1 [2ab3a3ac] LogExpFunctions v0.3.29 [e6f89c97] LoggingExtras v1.1.0 [c7f686f2] MCMCChains v7.1.0 [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 [76087f3c] NLopt v1.2.1 [77ba4419] NaNMath v1.1.3 [86f7a689] NamedArrays v0.10.4 [c020b1a1] NaturalSort v1.0.0 [b8a86587] NearestNeighbors v0.4.22 [6fe1bfb0] OffsetArrays v1.17.0 [429524aa] Optim v1.13.2 [3bd65402] Optimisers v0.4.6 [7f7a1694] Optimization v4.5.0 [3e6eede4] OptimizationBBO v0.4.1 [bca83a33] OptimizationBase v2.10.0 [4e6fcdb7] OptimizationNLopt v0.3.2 [36348300] OptimizationOptimJL v0.4.3 [bac558e1] OrderedCollections v1.8.1 [90014a1f] PDMats v0.11.35 [65ce6f38] PackageExtensionCompat v1.0.2 [d96e819e] Parameters v0.12.3 [69de0a69] Parsers v2.8.3 [85a6dd25] PositiveFactorizations v0.2.4 [aea7be01] PrecompileTools v1.3.2 [21216c6a] Preferences v1.5.0 [08abe8d2] PrettyTables v2.4.0 [33c8b6b6] ProgressLogging v0.1.5 [92933f4c] ProgressMeter v1.10.4 [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 v3.37.0 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [37e2e3b7] ReverseDiff v1.16.1 [708f8203] Richardson v1.4.2 [79098fc4] Rmath v0.8.0 [f2b01f46] Roots v2.2.8 [7e49a35a] RuntimeGeneratedFunctions v0.5.15 [26aad666] SSMProblems v0.5.1 [0bca4576] SciMLBase v2.110.0 [c0aeaf25] SciMLOperators v1.5.0 [53ae85a6] SciMLStructures v1.7.0 [30f210dd] ScientificTypesBase v3.0.0 [efcf1570] Setfield v1.1.2 [a2af1166] SortingAlgorithms v1.2.2 [9f842d2f] SparseConnectivityTracer v1.0.0 [dc90abb0] SparseInverseSubset v0.1.2 [0a514795] SparseMatrixColorings v0.4.21 [d4ead438] SpatialIndexing v0.1.6 [276daf66] SpecialFunctions v2.5.1 [171d559e] SplittablesBase v0.1.15 [860ef19b] StableRNGs v1.0.3 [90137ffa] StaticArrays v1.9.14 [1e83bf80] StaticArraysCore v1.4.3 [64bff920] StatisticalTraits v3.5.0 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.1 [2913bbd2] StatsBase v0.34.6 [4c63d2b9] StatsFuns v1.5.0 [5e0ebb24] Strided v2.3.2 [4db3bf67] StridedViews v0.4.1 [892a3eda] StringManipulation v0.4.1 [09ab397b] StructArrays v0.7.1 [2efcf032] SymbolicIndexingInterface v0.3.43 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [02d47bb6] TensorCast v0.4.9 [5d786b92] TerminalLoggers v0.1.7 [a759f4b9] TimerOutputs v0.5.29 [28d57a85] Transducers v0.4.84 [24ddb15e] TransmuteDims v0.1.17 [9d95972d] TupleTools v1.6.0 [fce5fe82] Turing v0.40.1 [3a884ed6] UnPack v1.0.2 [efce3f68] WoodburyMatrices v1.0.0 [700de1a5] ZygoteRules v0.2.7 [f5851436] FFTW_jll v3.3.11+0 [1d5cc7b8] IntelOpenMP_jll v2025.2.0+0 [81d17ec3] L_BFGS_B_jll v3.0.1+0 [856f044c] MKL_jll v2025.2.0+0 [079eb43e] NLopt_jll v2.10.0+0 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [f50d1b31] Rmath_jll v0.5.1+0 [1317d2d5] oneTBB_jll v2022.0.0+0 [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 v0.6.4 [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 v0.7.0 [9e88b42a] Serialization v1.11.0 [1a1011a3] SharedArrays v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.13.0 [f489334b] StyledStrings v1.11.0 [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 v8.15.0+1 [e37daf67] LibGit2_jll v1.9.1+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.7.15 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.5+0 [458c3c95] OpenSSL_jll v3.5.2+0 [efcefdf7] PCRE2_jll v10.45.0+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 v5.13.1+0 [8e850ede] nghttp2_jll v1.65.0+0 [3f19e933] p7zip_jll v17.5.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... Precompiling packages... 21971.1 ms ✓ Turing → TuringOptimExt 1 dependency successfully precompiled in 28 seconds. 300 already precompiled. Precompiling packages... 7300.7 ms ✓ HypothesisTests 1 dependency successfully precompiled in 10 seconds. 61 already precompiled. [ Info: [Turing]: progress logging is disabled globally Precompiling packages... 43123.4 ms ✓ ReverseDiff 1 dependency successfully precompiled in 44 seconds. 32 already precompiled. Precompiling packages... 20119.8 ms ✓ DistributionsAD → DistributionsADReverseDiffExt 1 dependency successfully precompiled in 23 seconds. 91 already precompiled. Precompiling packages... 19084.3 ms ✓ Bijectors → BijectorsReverseDiffExt 1 dependency successfully precompiled in 22 seconds. 91 already precompiled. Precompiling packages... 12654.5 ms ✓ LogDensityProblemsAD → LogDensityProblemsADReverseDiffExt 1 dependency successfully precompiled in 13 seconds. 37 already precompiled. Precompiling packages... 10423.0 ms ✓ ArrayInterface → ArrayInterfaceReverseDiffExt 1 dependency successfully precompiled in 11 seconds. 39 already precompiled. Precompiling packages... 10643.8 ms ✓ DifferentiationInterface → DifferentiationInterfaceReverseDiffExt 10474.5 ms ✓ OptimizationBase → OptimizationReverseDiffExt 2 dependencies successfully precompiled in 23 seconds. 112 already precompiled. Precompiling packages... 19398.3 ms ✓ Bijectors → BijectorsReverseDiffChainRulesExt 1 dependency successfully precompiled in 23 seconds. 111 already precompiled. [ 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/Me3pq/test/essential/container.jl:20 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.ContainerTests.var"#test#test##0", DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:510 [12] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/essential/container.jl:10 [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [14] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/essential/container.jl:21 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [16] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/essential/container.jl:25 [inlined] [17] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [18] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:33 [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [20] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:42 [inlined] [21] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [22] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:25 [inlined] [23] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [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:296 [27] _start() @ Base ./client.jl:563 fork: Error During Test at /home/pkgeval/.julia/packages/Turing/Me3pq/test/essential/container.jl:38 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.ContainerTests.var"#normal#normal##0", DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:510 [12] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/essential/container.jl:10 [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [14] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/essential/container.jl:39 [inlined] [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [16] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/essential/container.jl:51 [inlined] [17] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [18] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:33 [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [20] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:42 [inlined] [21] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [22] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:25 [inlined] [23] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [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:296 [27] _start() @ Base ./client.jl:563 models: Error During Test at /home/pkgeval/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:27 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#normal#normal##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}) @ Base ./array.jl:803 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:200 [16] kwcall(::@NamedTuple{initial_params::Nothing, nparticles::Int64}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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/Me3pq/src/mcmc/particle_mcmc.jl:186 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{nparticles::Int64}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [21] 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 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.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{nparticles::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:161 [27] sample @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:149 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:16 [32] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:13 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:28 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:36 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [38] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:25 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [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:296 [47] _start() @ Base ./client.jl:563 chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:53 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}) @ Base ./array.jl:803 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:200 [16] kwcall(::@NamedTuple{initial_params::Nothing, nparticles::Int64}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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/Me3pq/src/mcmc/particle_mcmc.jl:186 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{nparticles::Int64}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [21] 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 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.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{nparticles::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, 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::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:161 [27] sample @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:149 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:16 [32] test_chain_logp_metadata(spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/Me3pq/test/test_utils/sampler.jl:18 [33] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:13 [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:54 [inlined] [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:54 [inlined] [38] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [39] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:33 [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:46 [inlined] [42] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [43] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:25 [inlined] [44] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [45] top-level scope @ none:6 [46] eval(m::Module, e::Any) @ Core ./boot.jl:489 [47] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [48] _start() @ Base ./client.jl:563 logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:57 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#test#test##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}})(::Int64) @ Turing.Inference ./none:-1 [13] iterate @ ./generator.jl:48 [inlined] [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}) @ Base ./array.jl:803 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:200 [16] kwcall(::@NamedTuple{initial_params::Nothing, nparticles::Int64}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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/Me3pq/src/mcmc/particle_mcmc.jl:186 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{nparticles::Int64}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [21] 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 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.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{nparticles::Int64}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:161 [27] sample @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:149 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:16 [32] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:13 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:58 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:70 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [38] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:25 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [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:296 [47] _start() @ Base ./client.jl:563 chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:99 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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"#68#69"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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:803 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:325 [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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/Me3pq/src/mcmc/particle_mcmc.jl:312 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [21] 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 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.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{}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93 [27] sample @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:16 [32] test_chain_logp_metadata(spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}) @ Main.SamplerTestUtils ~/.julia/packages/Turing/Me3pq/test/test_utils/sampler.jl:18 [33] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:81 [34] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [35] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:100 [inlined] [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:100 [inlined] [38] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [39] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:33 [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:46 [inlined] [42] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [43] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:25 [inlined] [44] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [45] top-level scope @ none:6 [46] eval(m::Module, e::Any) @ Core ./boot.jl:489 [47] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [48] _start() @ Base ./client.jl:563 logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:103 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#test#test##1", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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:803 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:325 [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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/Me3pq/src/mcmc/particle_mcmc.jl:312 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [21] 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 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.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{}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93 [27] sample @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:16 [32] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:81 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:104 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:116 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [38] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:25 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [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:296 [47] _start() @ Base ./client.jl:563 reference particle: Error During Test at /home/pkgeval/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:124 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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:803 [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:325 [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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/Me3pq/src/mcmc/particle_mcmc.jl:312 [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [21] 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 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.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{}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93 [27] sample @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:22 [inlined] [30] #sample#111 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:19 [inlined] [31] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:16 [32] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:81 [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:125 [inlined] [35] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [36] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:125 [inlined] [37] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [38] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:33 [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:46 [inlined] [41] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [42] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:25 [inlined] [43] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [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:296 [47] _start() @ Base ./client.jl:563 addlogprob leads to reweighting: Error During Test at /home/pkgeval/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:130 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, 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}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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:803 [15] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:325 [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{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/Me3pq/src/mcmc/particle_mcmc.jl:312 [17] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116 [18] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined] [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState) @ Base.CoreLogging ./logging/logging.jl:540 [21] 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 [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] [24] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.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{}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [26] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93 [27] sample @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined] [28] #sample#112 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:31 [inlined] [29] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:22 [30] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:81 [31] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [32] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:133 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [34] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/particle_mcmc.jl:143 [inlined] [35] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [36] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:33 [37] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [38] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:46 [inlined] [39] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [40] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:25 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [42] top-level scope @ none:6 [43] eval(m::Module, e::Any) @ Core ./boot.jl:489 [44] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [45] _start() @ Base ./client.jl:563 ┌ Warning: The model does not contain any parameters. â”” @ DynamicPPL.DebugUtils ~/.julia/packages/DynamicPPL/bXCZJ/src/debug_utils.jl:305 [ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 2.0598561011023344) [ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.158304117994203) [ Info: Testing emcee with large number of iterations [ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 1.9758334846581072) [ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.176829569918974) [ Info: Starting ESS tests [ Info: Starting ESS inference tests [ Info: (symbol = :m, exact = 0.8, evaluated = 0.8172942592919593) [ Info: (symbol = "m[1]", exact = 0.0, evaluated = -0.02456171083886478) [ Info: (symbol = "m[2]", exact = 0.8, evaluated = 0.8075869528540663) gdemo with CSMC + ESS: Error During Test at /home/pkgeval/.julia/packages/Turing/Me3pq/test/mcmc/ess.jl:55 Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}, 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/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}, 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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}, 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}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#68#69"{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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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"#68#69"{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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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:803 [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::DynamicPPL.Sampler{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::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:325 [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.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::DynamicPPL.Sampler{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/Me3pq/src/mcmc/particle_mcmc.jl:312 [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::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116 [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::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::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99 [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{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{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::Nothing, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/gibbs.jl:459 [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::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{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{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/Me3pq/src/mcmc/gibbs.jl:429 [21] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/gibbs.jl:383 [22] step @ ~/.julia/packages/Turing/Me3pq/src/mcmc/gibbs.jl:371 [inlined] [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined] [24] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [25] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{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/7f1oY/src/logging.jl:157 [28] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/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::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{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{}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [30] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [31] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93 [32] sample @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined] [33] #sample#112 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:31 [inlined] [34] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:22 [35] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/mcmc/ess.jl:14 [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/ess.jl:42 [inlined] [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [39] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/ess.jl:56 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/ess.jl:57 [inlined] [42] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [43] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:33 [44] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [45] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:46 [inlined] [46] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [47] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:25 [inlined] [48] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [49] top-level scope @ none:6 [50] eval(m::Module, e::Any) @ Core ./boot.jl:489 [51] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [52] _start() @ Base ./client.jl:563 ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile ====================================================================================== cmd: /opt/julia/bin/julia 247 running 1 of 1 signal (10): User defined signal 1 TwoPhaseDefUseMap at ./array.jl:0 [inlined] PostOptAnalysisState at ./../usr/share/julia/Compiler/src/optimize.jl:641 [inlined] ipo_dataflow_analysis! at ./../usr/share/julia/Compiler/src/optimize.jl:995 optimize at ./../usr/share/julia/Compiler/src/optimize.jl:1022 jfptr_optimize_85321.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 finish_nocycle at ./../usr/share/julia/Compiler/src/typeinfer.jl:232 jfptr_finish_nocycle_86076.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 typeinf at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4528 typeinf_ext at ./../usr/share/julia/Compiler/src/typeinfer.jl:1385 typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1568 [inlined] typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1577 jfptr_typeinf_ext_toplevel_83133.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 jl_apply at /source/src/julia.h:2376 [inlined] jl_type_infer at /source/src/gf.c:462 jl_compile_method_internal at /source/src/gf.c:3514 _jl_invoke at /source/src/gf.c:4007 [inlined] ijl_apply_generic at /source/src/gf.c:4212 deepcopy_internal at ./deepcopy.jl:87 jfptr_deepcopy_internal_34772.1 at /opt/julia/share/julia/compiled/v1.13/Pkg/tUTdb_wscuQ.so (unknown line) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 deepcopy_internal at ./deepcopy.jl:87 jfptr_deepcopy_internal_34772.1 at /opt/julia/share/julia/compiled/v1.13/Pkg/tUTdb_wscuQ.so (unknown line) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 deepcopy_internal at ./deepcopy.jl:87 deepcopy at ./deepcopy.jl:34 [inlined] Trace at /home/pkgeval/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:508 unknown function (ip: 0x7f4e63339c17) at (unknown file) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 #68 at ./none (unknown line) iterate at ./generator.jl:48 [inlined] collect at ./array.jl:803 #initialstep#66 at /home/pkgeval/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:325 initialstep at /home/pkgeval/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:312 unknown function (ip: 0x7f4e63332ada) at (unknown file) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 #step#57 at /home/pkgeval/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116 step at /home/pkgeval/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99 unknown function (ip: 0x7f4e6331794e) at (unknown file) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 #gibbs_initialstep_recursive#76 at /home/pkgeval/.julia/packages/Turing/Me3pq/src/mcmc/gibbs.jl:459 unknown function (ip: 0x7f4e680fdcd8) at (unknown file) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/Me3pq/src/mcmc/gibbs.jl:429 gibbs_initialstep_recursive at /home/pkgeval/.julia/packages/Turing/Me3pq/src/mcmc/gibbs.jl:429 unknown function (ip: 0x7f4e680ed3ae) at (unknown file) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 #step#74 at /home/pkgeval/.julia/packages/Turing/Me3pq/src/mcmc/gibbs.jl:383 step at /home/pkgeval/.julia/packages/Turing/Me3pq/src/mcmc/gibbs.jl:371 [inlined] macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined] #25 at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 with_logstate at ./logging/logging.jl:540 unknown function (ip: 0x7f4e680d09fc) at (unknown file) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 with_logger at ./logging/logging.jl:651 with_progresslogger at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157 unknown function (ip: 0x7f4e827c574e) at (unknown file) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined] #mcmcsample#23 at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 mcmcsample at /home/pkgeval/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 unknown function (ip: 0x7f4e680b6a50) at (unknown file) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 #sample#56 at /home/pkgeval/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93 sample at /home/pkgeval/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined] #sample#112 at /home/pkgeval/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:31 [inlined] sample at /home/pkgeval/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:22 unknown function (ip: 0x7f4e68087edb) at (unknown file) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 jl_apply at /source/src/julia.h:2376 [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: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:558 eval_body at /source/src/interpreter.c:558 jl_interpret_toplevel_thunk at /source/src/interpreter.c:899 jl_toplevel_eval_flex at /source/src/toplevel.c:773 jl_eval_module_expr at /source/src/toplevel.c:196 [inlined] jl_toplevel_eval_flex at /source/src/toplevel.c:658 jl_toplevel_eval_flex at /source/src/toplevel.c:713 ijl_toplevel_eval at /source/src/toplevel.c:785 ijl_toplevel_eval_in at /source/src/toplevel.c:830 eval at ./boot.jl:489 include_string at ./loading.jl:2863 _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 _include at ./loading.jl:2923 include at ./Base.jl:309 IncludeInto at ./Base.jl:310 unknown function (ip: 0x7f4eb0134292) at (unknown file) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 jl_apply at /source/src/julia.h:2376 [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:708 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:558 jl_interpret_toplevel_thunk at /source/src/interpreter.c:899 jl_toplevel_eval_flex at /source/src/toplevel.c:773 jl_toplevel_eval_flex at /source/src/toplevel.c:713 ijl_toplevel_eval at /source/src/toplevel.c:785 ijl_toplevel_eval_in at /source/src/toplevel.c:830 eval at ./boot.jl:489 include_string at ./loading.jl:2863 _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 _include at ./loading.jl:2923 include at ./Base.jl:309 IncludeInto at ./Base.jl:310 jfptr_IncludeInto_61437.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 jl_apply at /source/src/julia.h:2376 [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:708 jl_interpret_toplevel_thunk at /source/src/interpreter.c:899 jl_toplevel_eval_flex at /source/src/toplevel.c:773 jl_toplevel_eval_flex at /source/src/toplevel.c:713 ijl_toplevel_eval at /source/src/toplevel.c:785 ijl_toplevel_eval_in at /source/src/toplevel.c:830 eval at ./boot.jl:489 exec_options at ./client.jl:296 _start at ./client.jl:563 jfptr__start_47667.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 jl_apply at /source/src/julia.h:2376 [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: 0x7f4eb14ad249) 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 ============================================================== MoGtest_default with CSMC + ESS: ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile ====================================================================================== 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:1192 wait_forever at ./task.jl:1129 jfptr_wait_forever_73644.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:4015 [inlined] ijl_apply_generic at /source/src/gf.c:4212 jl_apply at /source/src/julia.h:2376 [inlined] start_task at /source/src/task.c:1253 unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point ============================================================== Error During Test┌ 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()`. â”” @ Profile /opt/julia/share/julia/stdlib/v1.13/Profile/src/Profile.jl:1362 Overhead â•Ž [+additional indent] Count File:Line Function ========================================================= Thread 1 (default) Task 0x000077615cf09690 Total snapshots: 409. Utilization: 0% â•Ž409 @Base/task.jl:1129 wait_forever() 408â•Ž 409 @Base/task.jl:1192 wait() ┌ 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()`. â”” @ Profile /opt/julia/share/julia/stdlib/v1.13/Profile/src/Profile.jl:1362 at Overhead â•Ž [+additional indent] Count File:Line Function ========================================================= Thread 1 (default) Task 0x00007f4e97200010 Total snapshots: 46. Utilization: 100% â•Ž39 @Base/client.jl:563 _start() â•Ž 39 @Base/client.jl:296 exec_options(opts::Base.JLOptions) â•Ž 39 @Base/boot.jl:489 eval(m::Module, e::Any) â•Ž 39 @Base/Base.jl:310 (::Base.IncludeInto)(fname::String) â•Ž 39 @Base/Base.jl:309 include(mapexpr::Function, mod::Module, _path::Str… â•Ž 39 @Base/loading.jl:2923 _include(mapexpr::Function, mod::Module, _pat… â•Ž â•Ž 39 @Base/loading.jl:2863 include_string(mapexpr::typeof(identity), mo… â•Ž â•Ž 39 @Base/boot.jl:489 eval(m::Module, e::Any) â•Ž â•Ž 39 @Base/Base.jl:310 (::Base.IncludeInto)(fname::String) â•Ž â•Ž 39 @Base/Base.jl:309 include(mapexpr::Function, mod::Module, _path… â•Ž â•Ž 39 @Base/loading.jl:2923 _include(mapexpr::Function, mod::Module,… â•Ž â•Ž â•Ž 39 @Base/loading.jl:2863 include_string(mapexpr::typeof(identity… â•Ž â•Ž â•Ž 39 @Base/boot.jl:489 eval(m::Module, e::Any) â•Ž â•Ž â•Ž 39 @Turing/…actmcmc.jl:22 sample(rng::StableRNGs.LehmerRNG, mo… â•Ž â•Ž â•Ž 39 @Turing/…actmcmc.jl:31 #sample#112 â•Ž â•Ž â•Ž 39 @DynamicPPL/…er.jl:83 sample â•Ž â•Ž â•Ž â•Ž 39 @DynamicPPL/…er.jl:93 sample(rng::StableRNGs.LehmerRNG, … â•Ž â•Ž â•Ž â•Ž 39 @AbstractMCMC/…jl:109 kwcall(::@NamedTuple{chain_type::… â•Ž â•Ž â•Ž â•Ž 39 @AbstractMCMC/…jl:151 mcmcsample(rng::StableRNGs.Lehme… â•Ž â•Ž â•Ž â•Ž 39 @AbstractMCMC/…l:133 macro expansion â•Ž â•Ž â•Ž â•Ž 39 @AbstractMCMC/…l:157 with_progresslogger(f::Function… â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Base/…ogging.jl:651 with_logger(f::Function, logge… â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Base/…gging.jl:540 with_logstate(f::AbstractMCMC.… â•Ž â•Ž â•Ž â•Ž â•Ž 39 @AbstractMCMC/…:134 (::AbstractMCMC.var"#25#26"{N… â•Ž â•Ž â•Ž â•Ž â•Ž 39 @AbstractMCMC/…:171 macro expansion â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Turing/…bs.jl:371 step â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Turing/…bs.jl:383 step(rng::StableRNGs.Lehmer… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Turing/…s.jl:429 kwcall(::@NamedTuple{initia… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Turing/…s.jl:429 kwcall(::@NamedTuple{initi… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Turing/….jl:459 gibbs_initialstep_recursiv… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @DynamicPPL/…:99 kwcall(::@NamedTuple{init… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @DynamicPPL/…:116 step(rng::StableRNGs.Le… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Turing/….jl:312 kwcall(::@NamedTuple{in… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Turing/….jl:325 initialstep(rng::Stabl… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Base/…ay.jl:803 collect(itr::Base.Gen… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Base/…or.jl:48 iterate â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 none:? (::Turing.Inference.var"#68#6… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Turing/….jl:508 AdvancedPS.Trace(m… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Base/…py.jl:34 deepcopy â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Base/…py.jl:87 deepcopy_internal… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž 39 @Base/…py.jl:87 deepcopy_interna… 22â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +1 39 @Base/…py.jl:87 deepcopy_interna… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +2 17 @Compiler/…l:1577 typeinf_ext_to… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +3 16 @Compiler/…l:1568 typeinf_ext_to… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +4 1 @Compiler/…l:1379 typeinf_ext(in… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +5 1 @Compiler/…l:607 InferenceState â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +6 1 @Compiler/…l:602 InferenceState 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +7 1 @Compiler/…l:128 retrieve_code_i… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +4 15 @Compiler/…l:1385 typeinf_ext(in… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +5 8 @Compiler/…l:4521 typeinf(interp… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +6 8 @Compiler/…l:4363 typeinf_local(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +7 8 @Compiler/…l:3814 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +8 8 @Compiler/…l:3857 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +9 8 @Compiler/…l:3430 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +10 1 @Compiler/…l:3069 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +11 1 @Compiler/…l:3025 collect_argtyp… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +12 1 @Base/…ot.jl:648 Array 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +13 1 @Base/…ot.jl:588 GenericMemory â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +10 6 @Compiler/…l:3074 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +11 6 @Compiler/…l:3056 abstract_call(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +12 6 @Compiler/…l:2896 abstract_call â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +13 6 @Compiler/…l:2903 abstract_call(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +14 6 @Compiler/…l:2796 abstract_call_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +15 1 @Compiler/…l:120 abstract_call_g… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +16 1 @Compiler/…l:342 find_method_mat… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +17 1 @Compiler/…l:348 #find_method_ma… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +18 1 @Compiler/…l:386 find_simple_met… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +19 1 @Compiler/…l:102 findall â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +20 1 @Compiler/…l:105 findall(sig::Ty… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +21 1 @Compiler/…l:70 findall â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +22 1 @Compiler/…l:70 #findall#5 â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +23 1 @Compiler/…l:97 _findall 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +24 1 @Base/…ls.jl:1595 _methods_by_ft… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +15 5 @Compiler/…l:338 abstract_call_g… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +16 2 @Compiler/…l:167 (::Compiler.var… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +17 2 @Compiler/…l:735 abstract_call_m… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +18 1 @Compiler/…l:1036 typeinf_edge(i… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +19 1 @Base/…ls.jl:1769 specialize_met… 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +20 1 @Base/…ls.jl:1782 #specialize_me… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +18 1 @Compiler/…l:1094 typeinf_edge(i… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +19 1 @Compiler/…l:602 InferenceState 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +20 1 @Compiler/…l:128 retrieve_code_i… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +16 3 @Compiler/…l:252 (::Compiler.var… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +17 2 @Compiler/…l:178 (::Compiler.var… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +18 2 @Compiler/…l:868 abstract_call_m… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +19 2 @Compiler/…l:898 abstract_call_m… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +20 2 @Compiler/…l:1348 const_prop_cal… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +21 1 @Compiler/…l:4521 typeinf(interp… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +22 1 @Compiler/…l:4363 typeinf_local(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +23 1 @Compiler/…l:3814 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +24 1 @Compiler/…l:3857 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +25 1 @Compiler/…l:3430 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +26 1 @Compiler/…l:3074 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +27 1 @Compiler/…l:3056 abstract_call(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +28 1 @Compiler/…l:2896 abstract_call â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +29 1 @Compiler/…l:2903 abstract_call(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +30 1 @Compiler/…l:2796 abstract_call_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +31 1 @Compiler/…l:338 abstract_call_g… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +32 1 @Compiler/…l:252 (::Compiler.var… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +33 1 @Compiler/…l:178 (::Compiler.var… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +34 1 @Compiler/…l:868 abstract_call_m… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +35 1 @Compiler/…l:898 abstract_call_m… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +36 1 @Compiler/…l:1348 const_prop_cal… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +37 1 @Compiler/…l:4521 typeinf(interp… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +38 1 @Compiler/…l:4363 typeinf_local(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +39 1 @Compiler/…l:3814 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +40 1 @Compiler/…l:3857 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +41 1 @Compiler/…l:3430 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +42 1 @Compiler/…l:3074 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +43 1 @Compiler/…l:3056 abstract_call(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +44 1 @Compiler/…l:2896 abstract_call â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +45 1 @Compiler/…l:2903 abstract_call(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +46 1 @Compiler/…l:2796 abstract_call_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +47 1 @Compiler/…l:338 abstract_call_g… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +48 1 @Compiler/…l:252 (::Compiler.var… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +49 1 @Compiler/…l:178 (::Compiler.var… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +50 1 @Compiler/…l:868 abstract_call_m… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +51 1 @Compiler/…l:898 abstract_call_m… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +52 1 @Compiler/…l:1342 const_prop_cal… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +53 1 @Compiler/…l:607 InferenceState â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +54 1 @Compiler/…l:605 InferenceState â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +55 1 @Compiler/…l:366 Compiler.Infere… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +56 1 @Base/…et.jl:19 BitSet â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +57 1 @Base/…ot.jl:648 Array â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +58 1 @Base/…ot.jl:588 GenericMemory â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +21 1 @Compiler/…l:4528 typeinf(interp… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +22 1 @Compiler/…l:232 finish_nocycle(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +23 1 @Compiler/…l:1021 optimize(inter… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +24 1 @Compiler/…l:1048 run_passes_ipo… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +25 1 @Compiler/…l:1035 run_passes_ipo… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +26 1 @Compiler/…l:2146 compact! â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +27 1 @Compiler/…l:2146 compact!(code:… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +28 1 @Compiler/…l:807 IncrementalComp… 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +29 1 @Compiler/…l:784 Compiler.Increm… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +17 1 @Compiler/…l:219 (::Compiler.var… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +18 1 @Base/…ot.jl:648 Array 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +19 1 @Base/…ot.jl:588 GenericMemory â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +10 1 @Compiler/…l:3075 abstract_eval_… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +11 1 @Compiler/…l:1182 Compiler.Futur… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +12 1 @Base/…ay.jl:1321 push! 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +13 1 @Base/…ay.jl:1017 __safe_setinde… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +5 7 @Compiler/…l:4528 typeinf(interp… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +6 1 @Compiler/…l:229 finish_nocycle(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +7 1 @Compiler/…l:622 finishinfer!(me… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +8 1 @Compiler/…l:875 type_annotate!(… 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +9 1 @Base/…ls.jl:973 setindex! â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +6 5 @Compiler/…l:232 finish_nocycle(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +7 5 @Compiler/…l:1021 optimize(inter… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +8 5 @Compiler/…l:1048 run_passes_ipo… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +9 5 @Compiler/…l:1035 run_passes_ipo… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +10 1 @Compiler/…l:1330 slot2reg â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +11 1 @Compiler/…l:476 construct_domtr… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +12 1 @Compiler/…l:477 construct_domtr… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +13 1 @Compiler/…l:242 construct_domtr… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +14 1 @Compiler/…l:259 update_domtree! â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +15 1 @Compiler/…l:319 SNCA!(domtree::… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +16 1 @Base/…ay.jl:1503 resize!(a::Vec… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +17 1 @Base/…ay.jl:1181 _growend! â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +18 1 @Base/…ay.jl:1156 _growend_inter… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +19 1 @Base/…ay.jl:1076 array_new_memo… 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +20 1 @Base/…ot.jl:588 GenericMemory â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +10 3 @Compiler/…l:76 ssa_inlining_pas… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +11 3 @Compiler/…l:1664 assemble_inlin… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +12 3 @Compiler/…l:1413 handle_call! â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +13 3 @Compiler/…l:1381 compute_inlini… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +14 3 @Compiler/…l:1309 handle_any_con… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +15 2 @Compiler/…l:1318 handle_any_con… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +16 2 @Compiler/…l:1435 handle_const_p… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +17 2 @Compiler/â/home/pkgeval/.julia/packages/Turing/Me3pq/test/mcmc/ess.jl:61 €¦l:1442 handle_const_p… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +18 1 @Compiler/…l:868 resolve_todo(mi… 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +19 1 @Base/…on.jl:0 src_inlining_poli… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +18 1 @Compiler/…l:875 resolve_todo(mi… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +19 1 @Compiler/…l:985 retrieve_ir_for… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +20 1 @Compiler/…l:972 retrieve_ir_for… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +21 1 @Compiler/…l:449 copy(ir::Compil… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +22 1 @Compiler/…l:284 copy(is::Compil… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +23 1 @Base/…ay.jl:360 copy 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +24 1 @Base/…ot.jl:588 GenericMemory â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +15 1 @Compiler/…l:1321 handle_any_con… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +16 1 @Compiler/…l:1421 handle_match! â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +17 1 @Compiler/…l:1429 handle_match!(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +18 1 @Compiler/…l:921 analyze_method! â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +19 1 @Compiler/…l:954 analyze_method!… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +20 1 @Compiler/…l:873 resolve_todo(mi… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +21 1 @Compiler/…l:959 retrieve_ir_for… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +22 1 @Compiler/…l:20 inflate_ir!(ci::… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +23 1 @Compiler/…l:24 inflate_ir!(ci::… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +24 1 @Compiler/…l:96 compute_basic_bl… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +25 1 @Compiler/…l:47 basic_blocks_sta… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +26 1 @Base/…et.jl:19 BitSet â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +27 1 @Base/…ot.jl:648 Array 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +28 1 @Base/…ot.jl:588 GenericMemory â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +10 1 @Compiler/…l:79 ssa_inlining_pas… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +11 1 @Compiler/…l:663 batch_inline!(i… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +12 1 @Compiler/…l:438 ir_inline_item!… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +13 1 @Compiler/…l:1871 iterate â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +14 1 @Compiler/…l:1949 iterate_compac… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +15 1 @Compiler/…l:1464 process_node!(… 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +16 1 @Compiler/…l:365 already_inserte… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +6 1 @Compiler/…l:236 finish_nocycle(… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +7 1 @Compiler/…l:127 finish!(interp:… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +8 1 @Compiler/…l:421 transform_resul… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +9 1 @Compiler/…l:350 compute_inlinin… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +10 1 @Compiler/…l:399 inline_cost_mod… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +11 1 @Compiler/…l:1479 inline_cost_mo… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +12 1 @Compiler/…l:1457 statement_or_b… 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +13 1 @Compiler/…l:1348 statement_cost… â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +3 1 @Compiler/…l:1569 typeinf_ext_to… 1â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž â•Ž +4 1 @Compiler/…l:1562 add_codeinsts_… Got exception outside of a @test FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds` Stacktrace: [1] getproperty @ ./Base_compiler.jl:57 [inlined] [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock}) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152 [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode) @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289 [4] build_callable(sig::Type{Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}, 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/Sf4tJ/src/copyable_task.jl:89 [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282 [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}) @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279 [7] #TapedTask#58 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:95 [inlined] [8] TapedTask @ ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:94 [inlined] [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}, 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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}, 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}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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}}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:510 [12] (::Turing.Inference.var"#68#69"{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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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"#68#69"{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.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, 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:803 [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::DynamicPPL.Sampler{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::Nothing}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/particle_mcmc.jl:325 [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.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::DynamicPPL.Sampler{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/Me3pq/src/mcmc/particle_mcmc.jl:312 [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::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116 [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::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::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99 [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{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{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::Nothing, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/gibbs.jl:459 [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::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{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{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/Me3pq/src/mcmc/gibbs.jl:429 [21] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.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{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}; initial_params::Nothing, kwargs::@Kwargs{}) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/gibbs.jl:383 [22] step @ ~/.julia/packages/Turing/Me3pq/src/mcmc/gibbs.jl:371 [inlined] [23] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined] [24] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.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{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, Int64, Float64, Int64, Int64})() @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134 [25] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.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{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{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/7f1oY/src/logging.jl:157 [28] macro expansion @ ~/.julia/packages/AbstractMCMC/7f1oY/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::DynamicPPL.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{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{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{}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151 [30] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.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{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109 [31] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.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{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{}) @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93 [32] sample @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined] [33] #sample#112 @ ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:31 [inlined] [34] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, alg::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{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}, N::Int64) @ Turing.Inference ~/.julia/packages/Turing/Me3pq/src/mcmc/abstractmcmc.jl:22 [35] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/mcmc/ess.jl:14 [36] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [37] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/ess.jl:42 [inlined] [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [39] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/ess.jl:62 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [41] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/mcmc/ess.jl:67 [inlined] [42] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [43] top-level scope @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:33 [44] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [45] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:46 [inlined] [46] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1929 [inlined] [47] macro expansion @ ~/.julia/packages/Turing/Me3pq/test/runtests.jl:25 [inlined] [48] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:309 [49] top-level scope @ none:6 [50] eval(m::Module, e::Any) @ Core ./boot.jl:489 [51] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [52] _start() @ Base ./client.jl:563 [247] signal 15: Terminated in expression starting at /home/pkgeval/.julia/packages/Turing/Me3pq/test/mcmc/ess.jl:13 _ZN4llvm36MachineOptimizationRemarkEmitterPass20runOnMachineFunctionERNS_15MachineFunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line) unknown function (ip: 0xf34354f) at (unknown file) unknown function (ip: (nil)) at (unknown file) Allocations: 918019372 (Pool: 918014436; Big: 4936); GC: 220 PkgEval terminated after 2723.52s: test duration exceeded the time limit