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OK, it broke in a place I did not expect it to break in: Failed to run notebook: /home/runner/work/docs/docs/tutorials/bayesian-differential-equations/index.qmd
The underlying Julia error was:
EvaluationError: Encountered 2 errors during evaluation
Error 1 of 2
@ /home/runner/work/docs/docs/tutorials/bayesian-differential-equations/index.qmd:183
DomainError with Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}(-3.2843921748373545e167,0.0,0.0,4.249067788643655e103,0.0,0.0):
Poisson: the condition λ >= zero(λ) is not satisfied.
Stacktrace:
[1] #154
@ ~/.julia/packages/Distributions/PVuFU/src/univariate/discrete/poisson.jl:30 [inlined]
[2] check_args
@ ~/.julia/packages/Distributions/PVuFU/src/utils.jl:89 [inlined]
[3] #Poisson#152
@ ~/.julia/packages/Distributions/PVuFU/src/univariate/discrete/poisson.jl:30 [inlined]
[4] Poisson
@ ~/.julia/packages/Distributions/PVuFU/src/univariate/discrete/poisson.jl:29 [inlined]
[5] _broadcast_getindex_evalf
@ ./broadcast.jl:699 [inlined]
[6] _broadcast_getindex
@ ./broadcast.jl:672 [inlined]
[7] _getindex
@ ./broadcast.jl:620 [inlined]
[8] getindex
@ ./broadcast.jl:616 [inlined]
[9] macro expansion
@ ./broadcast.jl:995 [inlined]
[10] macro expansion
@ ./simdloop.jl:77 [inlined]
[11] copyto!
@ ./broadcast.jl:994 [inlined]
[12] copyto!
@ ./broadcast.jl:947 [inlined]
[13] copy
@ ./broadcast.jl:919 [inlined]
[14] materialize(bc::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1}, Nothing, Type{Poisson}, Tuple{Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1}, Nothing, typeof(+), Tuple{Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1}, Nothing, typeof(*), Tuple{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 5}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 5}}}}, Float64}}}})
@ Base.Broadcast ./broadcast.jl:894
[15] fitlv2(__model__::DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.InitContext{TaskLocalRNG, DynamicPPL.InitFromVector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 5}}, VarNamedTuple{(:α, :β, :γ, :δ, :q), NTuple{5, DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll}, DynamicPPL.LinkAll}, false}, __varinfo__::DynamicPPL.OnlyAccsVarInfo{DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::Dyna
@ Main.Notebook ~/work/docs/docs/tutorials/bayesian-differential-equations/index.qmd:198
[16] _evaluate!!
@ ~/.julia/packages/DynamicPPL/TCkGV/src/model.jl:907 [inlined]
[17] evaluate_nowarn!!
@ ~/.julia/packages/DynamicPPL/TCkGV/src/model.jl:893 [inlined]
[18] init!!
@ ~/.julia/packages/DynamicPPL/TCkGV/src/model.jl:803 [inlined]
[19] init!!
@ ~/.julia/packages/DynamicPPL/TCkGV/src/model.jl:811 [inlined]
[20] logdensity_at(params::Vector{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}, Float64, 5}}, model::DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, getlogdensity::typeof(DynamicPPL.getlogjoint_internal), varname_ranges::VarNamedTuple{(:α, :β, :γ, :δ, :q), NTuple{5, DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, transform_strategy::DynamicPPL.LinkAll, accs::DynamicPPL.AccumulatorTuple
@ DynamicPPL ~/.julia/packages/DynamicPPL/TCkGV/src/logdensityfunction.jl:483
[21] FixTail
@ ~/.julia/packages/DifferentiationInterface/afUhd/src/utils/context.jl:172 [inlined]
[22] vector_mode_dual_eval!
@ ~/.julia/packages/ForwardDiff/z3jRk/src/apiutils.jl:24 [inlined]
[23] vector_mode_gradient!(result::DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, f::DifferentiationInterface.FixTail{typeof(DynamicPPL.logdensity_at), Tuple{DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, typeof(DynamicPPL.getlogjoint_internal), VarNamedTuple{(:α, :β, :γ, :δ, :q), NTuple{5, DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, DynamicPPL.LinkAll, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobi
@ ForwardDiff ~/.julia/packages/ForwardDiff/z3jRk/src/gradient.jl:105
[24] gradient!
@ ~/.julia/packages/ForwardDiff/z3jRk/src/gradient.jl:39 [inlined]
[25] value_and_gradient
@ ~/.julia/packages/DifferentiationInterface/afUhd/ext/DifferentiationInterfaceForwardDiffExt/onearg.jl:419 [inlined]
[26] logdensity_and_gradient
@ ~/.julia/packages/DynamicPPL/TCkGV/src/logdensityfunction.jl:558 [inlined]
[27] Fix
@ ./operators.jl:1193 [inlined]
[28] ∂H∂θ
@ ~/.julia/packages/AdvancedHMC/kEVkt/src/hamiltonian.jl:46 [inlined]
[29] step(lf::AdvancedHMC.Leapfrog{Float64}, h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, AutoForwardDiff{5, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), VarNamedTuple{(:α, :β, :γ, :δ, :q), NTuple{5, DynamicPPL.RangeAndTransform{DynamicP
@ AdvancedHMC ~/.julia/packages/AdvancedHMC/kEVkt/src/integrator.jl:244
[30] step
@ ~/.julia/packages/AdvancedHMC/kEVkt/src/integrator.jl:216 [inlined]
[31] build_tree(rng::TaskLocalRNG, nt::AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}, h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, AutoForwardDiff{5, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.Li
@ AdvancedHMC ~/.julia/packages/AdvancedHMC/kEVkt/src/trajectory.jl:640
[32] transition(rng::TaskLocalRNG, h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, AutoForwardDiff{5, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}, DynamicPPL.LinkAll, typeof(DynamicPPL.getlogjoint_internal), VarNamedTuple{(:α, :β, :γ, :δ, :q), NTuple{5, DynamicPPL.RangeAndTransform{DynamicPPL.DynamicLink}}}, D
@ AdvancedHMC ~/.julia/packages/AdvancedHMC/kEVkt/src/trajectory.jl:702
[33] transition
@ ~/.julia/packages/AdvancedHMC/kEVkt/src/sampler.jl:57 [inlined]
[34] #step#28
@ ~/.julia/packages/Turing/pH0SD/src/mcmc/hmc.jl:218 [inlined]
[35] _step_or_step_warmup(::Int64, ::Int64, ::TaskLocalRNG, ::Vararg{Any}; kwargs::@Kwargs{discard_sample::Bool, nadapts::Int64, initial_params::InitFromUniform{Float64}, chain_number::Int64})
@ AbstractMCMC ~/.julia/packages/AbstractMCMC/C1aKp/src/sample.jl:128
[36] _step_or_step_warmup
@ ~/.julia/packages/AbstractMCMC/C1aKp/src/sample.jl:124 [inlined]
[37] macro expansion
@ ~/.julia/packages/AbstractMCMC/C1aKp/src/sample.jl:255 [inlined]
[38] (::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64, initial_params::InitFromUniform{Float64}, chain_number::Int64}, TaskLocalRNG, DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Int64, Float64, Int64})()
@ AbstractMCMC ~/.julia/packages/AbstractMCMC/C1aKp/src/logging.jl:134
[39] with_logstate(f::AbstractMCMC.var"#29#30"{Nothing, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64, initial_params::InitFromUniform{Float64}, chain_number::Int64}, TaskLocalRNG, DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Int64, Float64, Int64}, logstate::Base.CoreLogging.LogState)
@ Base.CoreLogging ./logging/logging.jl:542
[40] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
@ Base.CoreLogging ./logging/logging.jl:653
[41] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
@ AbstractMCMC ~/.julia/packages/AbstractMCMC/C1aKp/src/logging.jl:157
[42] macro expansion
@ ~/.julia/packages/AbstractMCMC/C1aKp/src/logging.jl:133 [inlined]
[43] mcmcsample(rng::TaskLocalRNG, model::DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing,
@ AbstractMCMC ~/.julia/packages/AbstractMCMC/C1aKp/src/sample.jl:204
[44] #sample#26
@ ~/.julia/packages/Turing/pH0SD/src/mcmc/hmc.jl:118 [inlined]
[45] (::AbstractMCMC.var"#sample_chain#109"{String, TaskLocalRNG, DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Int64, Int64})(i::Int64, seed::UInt64, initial_params::InitFromUniform{Float64}, initial_state::Nothing)
@ AbstractMCMC ~/.julia/packages/AbstractMCMC/C1aKp/src/sample.jl:893
[46] #Generator##0
@ ./generator.jl:37 [inlined]
[47] iterate
@ ./generator.jl:48 [inlined]
[48] collect_to!(dest::Vector{Chains{Float64, AxisArrays.AxisArray{Float64, 3, Array{Float64, 3}, Tuple{AxisArrays.Axis{:iter, StepRange{Int64, Int64}}, AxisArrays.Axis{:var, Vector{Symbol}}, AxisArrays.Axis{:chain, UnitRange{Int64}}}}, Missing, @NamedTuple{parameters::Vector{Symbol}, internals::Vector{Symbol}}, @NamedTuple{varname_to_symbol::OrderedDict{AbstractPPL.VarName, Symbol}, start_time::Float64, stop_time::Float64}}}, itr::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}, Vector{InitFromUniform{Float64}}, Vector{Nothing}}}, Base.var"#Generator##0#Generator##1"{AbstractMCMC.var"#sample_chain#109"{String, TaskLocalRNG, DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing,
@ Base ./array.jl:848
[49] collect_to_with_first!(dest::Vector{Chains{Float64, AxisArrays.AxisArray{Float64, 3, Array{Float64, 3}, Tuple{AxisArrays.Axis{:iter, StepRange{Int64, Int64}}, AxisArrays.Axis{:var, Vector{Symbol}}, AxisArrays.Axis{:chain, UnitRange{Int64}}}}, Missing, @NamedTuple{parameters::Vector{Symbol}, internals::Vector{Symbol}}, @NamedTuple{varname_to_symbol::OrderedDict{AbstractPPL.VarName, Symbol}, start_time::Float64, stop_time::Float64}}}, v1::Chains{Float64, AxisArrays.AxisArray{Float64, 3, Array{Float64, 3}, Tuple{AxisArrays.Axis{:iter, StepRange{Int64, Int64}}, AxisArrays.Axis{:var, Vector{Symbol}}, AxisArrays.Axis{:chain, UnitRange{Int64}}}}, Missing, @NamedTuple{parameters::Vector{Symbol}, internals::Vector{Symbol}}, @NamedTuple{varname_to_symbol::OrderedDict{AbstractPPL.VarName, Symbol}, start_time::Float64, stop_time::Float64}}, itr::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}, Vecto
@ Base ./array.jl:826
[50] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}, Vector{InitFromUniform{Float64}}, Vector{Nothing}}}, Base.var"#Generator##0#Generator##1"{AbstractMCMC.var"#sample_chain#109"{String, TaskLocalRNG, DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, Int64, Int64}}})
@ Base ./array.jl:800
[51] map
@ ./abstractarray.jl:3526 [inlined]
[52] mcmcsample(rng::TaskLocalRNG, model::DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, sampler::NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, ::MCMCSerial, N::Int64, nchains::Int64; progressname::String, initial_params::Vector{InitFromUniform{Float64}}, initial_state::Nothing, kwargs::@Kwargs{chain_type::UnionAll, check_model::Bool, prog
@ AbstractMCMC ~/.julia/packages/AbstractMCMC/C1aKp/src/sample.jl:906
[53] sample(rng::TaskLocalRNG, model::DynamicPPL.Model{var"#fitlv2", (:data, :prob), (), (), Tuple{Vector{Int64}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext, false}, spl::NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, ensemble::MCMCSerial, N::Int64, n_chains::Int64; chain_type::Type, check_model::Bool, initial_params::Vector{InitFromUniform{Float64}}, kwargs::@Kwargs{progress::Bool})
@ Turing.Inference ~/.julia/packages/Turing/pH0SD/src/mcmc/abstractmcmc.jl:134
[54] sample
@ ~/.julia/packages/Turing/pH0SD/src/mcmc/abstractmcmc.jl:117 [inlined]
[55] #sample#4
@ ~/.julia/packages/Turing/pH0SD/src/mcmc/abstractmcmc.jl:112 [inlined] |
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Mostly want to see whether it breaks totally or not, and if not, how long does it take to run