# Can we use LinearExponential with Turing.jl model?
**URL:** https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479
**Category:** Modelling & Simulations
**Tags:** diffeq, sciml
**Created:** [October 9, 2022, 2:38pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479 "2022-10-09T14:38:44Z")
**Posts on this page:** 19
**Page:** 1
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 9, 2022, 2:38pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/1 "2022-10-09T14:38:44Z")
Trying to use the `LinearExponential` solver with a Turing.jl model to run a Bayesian inference. Can this example work?
Model:
```julia
dat_test = [0.0,4.4,6.9,8.2,7.8,7.5,6.2,5.3,4.9,3.7,1.05]
times = [0.0,0.27,0.58,1.02,2.02,3.62,5.08,7.07,9.0,12.15,24.17]
K_ = zeros(2,2)
@model function fitPKIndLin(data)
# priors
σ ~ truncated(Cauchy(0.0, 0.5), 0.0, 2.0)
ka ~ LogNormal(log(2.0), 0.2)
CL ~ LogNormal(log(4.0), 0.2)
V ~ LogNormal(log(35.0), 0.2)
K_[1,1] = -ka
K_[2,1] = ka/V
K_[2,2] = -(CL/V)
K = DiffEqArrayOperator(K_)
prob = ODEProblem(K, [300.0,0.0], (0.0,24.17))
predicted = Array(solve(prob, LinearExponential(), tstops = times))[2, :]
# likelihood
for i = 1:length(predicted)
data[i] ~ Normal(max(predicted[i], 1e-12), σ)
end
end
model_lin = fitPKIndLin(dat_test)
@time chain_lin = sample(model_lin, NUTS(250, .65), MCMCSerial(), 250, 4)
```
When I run this I get an error associated with the `K_[1,1] = -ka` line:
```julia
ERROR: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}
, Float64, 4}) Closest candidates are:
(::Type{T})(::Real, ::RoundingMode) where T<:AbstractFloat at rounding.jl:200
(::Type{T})(::T) where T<:Number at boot.jl:772
(::Type{T})(::AbstractChar) where T<:Union{AbstractChar, Number} at char.jl:50
...
Stacktrace:
[1] convert(#unused#::Type{Float64}, x::ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float
64, 4}) @ Base ./number.jl:7
[2] setindex!(::Matrix{Float64}, ::ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4
}, ::Int64, ::Int64) @ Base ./array.jl:968
[3] fitPKIndLin( __model__ ::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}},
Tuple{}, DynamicPPL.DefaultContext}, __varinfo__ ::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Base.RefValue{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, __context__ ::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random._GLOBAL_RNG}, data::Vector{Float64}) @ Main ~/projects/ghe/pmx-in-julia/TheophPK9.jl:513
[4] macro expansion
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:593 [inlined]
[5] _evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:576 [inlined]
[6] evaluate_threadsafe!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:567 [inlined]
[7] evaluate!!(model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tupl
e{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, context::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random._GLOBAL_RNG}) @ DynamicPPL ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:502
[8] evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:515 [inlined]
[9] evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:523 [inlined]
[10] LogDensityFunction
@ ~/.julia/packages/Turing/szPqN/src/Turing.jl:38 [inlined]
[11] logdensity
@ ~/.julia/packages/Turing/szPqN/src/Turing.jl:42 [inlined]
[12] Fix1
@ ./operators.jl:1096 [inlined]
[13] vector_mode_dual_eval!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/apiutils.jl:37 [inlined]
[14] vector_mode_gradient!(result::DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, f::Ba
se.Fix1{typeof(LogDensityProblems.logdensity), Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}}, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}) @ ForwardDiff ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:113
[15] gradient!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:37 [inlined]
[16] gradient!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:35 [inlined]
[17] logdensity_and_gradient
@ ~/.julia/packages/LogDensityProblems/oAYeE/src/AD_ForwardDiff.jl:49 [inlined]
[18] ∂logπ∂θ
@ ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:166 [inlined]
[19] ∂H∂θ(h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, Base.Fix1{t
ypeof(LogDensityProblems.logdensity), LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}, Turing.Inference.var"#∂logπ∂θ#44"{LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}}, θ::Vector{Float64}) @ AdvancedHMC ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:31
[20] phasepoint
@ ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:76 [inlined]
[21] phasepoint(rng::Random._GLOBAL_RNG, θ::Vector{Float64}, h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuc
lideanMetric{Float64, Vector{Float64}}, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}, Turing.Inference.var"#∂logπ∂θ#44"{LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}}) @ AdvancedHMC ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:153
[22] initialstep(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tu
ple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, vi::DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}; init_params::Nothing, nadapts::Int64, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:170
[23] step(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vec
tor{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}; resume_from::Nothing, init_params::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}}) @ DynamicPPL ~/.julia/packages/DynamicPPL/zPOYL/src/sampler.jl:104
[24] macro expansion
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:120 [inlined]
[25] macro expansion
@ ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:328 [inlined]
[26] macro expansion
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/logging.jl:9 [inlined]
[27] mcmcsample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tup
le{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, discard_initial::Int64, thinning::Int64, chain_type::Type, kwargs::Base.Pairs{Symbol, Union{Nothing, Int64}, Tuple{Symbol, Symbol}, NamedTuple{(:nadapts, :init_params), Tuple{Int64, Nothing}}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:111
[28] #sample#42
@ ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:133 [inlined]
[29] (::AbstractMCMC.var"#sample_chain#78"{String, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTupl
e{(:chain_type, :progress), Tuple{UnionAll, Bool}}}, Random._GLOBAL_RNG, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64})(i::Int64, seed::UInt64, init_params::Nothing) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:506
[30] sample_chain
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:503 [inlined]
[31] #4
@ ./generator.jl:36 [inlined]
[32] iterate
@ ./generator.jl:47 [inlined]
[33] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}}}, Base.var"#4#5
"{AbstractMCMC.var"#sample_chain#78"{String, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTuple{(:chain_type, :progress), Tuple{UnionAll, Bool}}}, Random._GLOBAL_RNG, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64}}}) @ Base ./array.jl:787
[34] map
@ ./abstractarray.jl:3055 [inlined]
[35] mcmcsample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tup
le{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, ::MCMCSerial, N::Int64, nchains::Int64; progressname::String, init_params::Nothing, kwargs::Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTuple{(:chain_type, :progress), Tuple{UnionAll, Bool}}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:518
[36] sample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{V
ector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, ensemble::MCMCSerial, N::Int64, n_chains::Int64; chain_type::Type, progress::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:220
[37] sample
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:209 [inlined]
[38] #sample#6
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:205 [inlined]
[39] sample
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:196 [inlined]
[40] #sample#5
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:192 [inlined]
[41] sample(model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{},
DynamicPPL.DefaultContext}, alg::NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}, ensemble::MCMCSerial, N::Int64, n_chains::Int64) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:184
[42] top-level scope
@ ./timing.jl:262 [inlined]
[43] top-level scope
@ ~/projects/ghe/pmx-in-julia/TheophPK9.jl:0
```
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 9, 2022, 2:40pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/2 "2022-10-09T14:40:28Z")
@ChrisRackauckas what do you think?
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [October 9, 2022, 2:49pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/3 "2022-10-09T14:49:33Z")
If your ODE is defined with dual numbers in the parameterization, then you need dual numbers in the propagation of state. In other words, `u0 = typeof(ka).([300.0,0.0])` would at least be required. Try that.
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 9, 2022, 3:09pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/4 "2022-10-09T15:09:49Z")
Hmm. I did something like the code below but still getting same error! Was that what you had in mind?
```julia
u0 = typeof(ka).([300.0,0.0])
prob = ODEProblem(K, [300.0,0.0], (0.0,24.17))
```
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 9, 2022, 3:12pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/5 "2022-10-09T15:12:59Z")
If I just drop defining the `K` matrix beforehand and update the code to the one below, it does not complain about assigning the parameters to the K\_ matrix anymore.
```julia
@model function fitPKIndLin(data)
# priors
σ ~ truncated(Cauchy(0.0, 0.5), 0.0, 2.0)
ka ~ LogNormal(log(2.0), 0.2)
CL ~ LogNormal(log(4.0), 0.2)
V ~ LogNormal(log(35.0), 0.2)
K_ = [-ka 0.0; ka/V -(CL/V)]
K = DiffEqArrayOperator(K_)
prob = ODEProblem(K, [300.0,0.0], (0.0,24.17))
predicted = Array(solve(prob, LinearExponential(), tstops = times))[2, :]
# likelihood
for i = 1:length(predicted)
data[i] ~ Normal(max(predicted[i], 1e-12), σ)
end
end
```
However it complains at the solver step (`predicted = Array(solve(prob, LinearExponential(), tstops = times))[2, :]`) with this error:
```julia
ERROR: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}
, Float64, 4}) Closest candidates are:
(::Type{T})(::Real, ::RoundingMode) where T<:AbstractFloat at rounding.jl:200
(::Type{T})(::T) where T<:Number at boot.jl:772
(::Type{T})(::AbstractChar) where T<:Union{AbstractChar, Number} at char.jl:50
...
Stacktrace:
[1] convert(#unused#::Type{Float64}, x::ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float
64, 4}) @ Base ./number.jl:7
[2] setindex!(A::Vector{Float64}, x::ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64,
4}, i1::Int64) @ Base ./array.jl:966
[3] generic_matvecmul!(C::Vector{Float64}, tA::Char, A::Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.Tur
ingTag, Float64}, Float64, 4}}, B::Vector{Float64}, _add::LinearAlgebra.MulAddMul{true, true, Bool, Bool}) @ LinearAlgebra /Applications/Julia-1.8.app/Contents/Resources/julia/share/julia/stdlib/v1.8/LinearAlgeb
ra/src/matmul.jl:805 [4] mul!
@ /Applications/Julia-1.8.app/Contents/Resources/julia/share/julia/stdlib/v1.8/LinearAlgebra/src/matmul.
jl:81 [inlined] [5] mul!
@ /Applications/Julia-1.8.app/Contents/Resources/julia/share/julia/stdlib/v1.8/LinearAlgebra/src/matmul.
jl:276 [inlined] [6] mul!
@ ~/.julia/packages/SciMLBase/xWByK/src/operators/common_defaults.jl:35 [inlined]
[7] DiffEqArrayOperator
@ ~/.julia/packages/SciMLBase/xWByK/src/operators/basic_operators.jl:178 [inlined]
[8] ODEFunction
@ ~/.julia/packages/SciMLBase/xWByK/src/scimlfunctions.jl:1962 [inlined]
[9] initialize!(integrator::OrdinaryDiffEq.ODEIntegrator{LinearExponential, true, Vector{Float64}, Nothing
, Float64, SciMLBase.NullParameters, Float64, Float64, Float64, Float64, Vector{Vector{Float64}}, ODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{Float64}}}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, LinearExponential, OrdinaryDiffEq.InterpolationData{ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Vector{Vector{Float64}}, Vector{Float64}, Vector{Vector{Vector{Float64}}}, OrdinaryDiffEq.LinearExponentialCache{Vector{Float64}, Vector{Float64}, Nothing}}, DiffEqBase.DEStats}, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, OrdinaryDiffEq.LinearExponentialCache{Vector{Float64}, Vector{Float64}, Nothing}, OrdinaryDiffEq.DEOptions{Float64, Float64, Float64, Float64, PIController{Int64}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(opnorm), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, Nothing, Nothing, Int64, Vector{Float64}, Tuple{}, Tuple{}}, Vector{Float64}, Float64, Nothing, OrdinaryDiffEq.DefaultInit}, cache::OrdinaryDiffEq.LinearExponentialCache{Vector{Float64}, Vector{Float64}, Nothing}) @ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/vfMzV/src/perform_step/linear_perform_step.jl:717
[10] __init(prob::ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEF
unction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, alg::LinearExponential, timeseries_init::Tuple{}, ts_init::Tuple{}, ks_init::Tuple{}, recompile::Type{Val{true}}; saveat::Tuple{}, tstops::Vector{Float64}, d_discontinuities::Tuple{}, save_idxs::Nothing, save_everystep::Bool, save_on::Bool, save_start::Bool, save_end::Nothing, callback::Nothing, dense::Bool, calck::Bool, dt::Float64, dtmin::Nothing, dtmax::Float64, force_dtmin::Bool, adaptive::Bool, gamma::Int64, abstol::Nothing, reltol::Nothing, qmin::Int64, qmax::Int64, qsteady_min::Int64, qsteady_max::Int64, beta1::Nothing, beta2::Nothing, qoldinit::Int64, controller::Nothing, fullnormalize::Bool, failfactor::Int64, maxiters::Int64, internalnorm::typeof(DiffEqBase.ODE_DEFAULT_NORM), internalopnorm::typeof(opnorm), isoutofdomain::typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), unstable_check::typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), verbose::Bool, timeseries_errors::Bool, dense_errors::Bool, advance_to_tstop::Bool, stop_at_next_tstop::Bool, initialize_save::Bool, progress::Bool, progress_steps::Int64, progress_name::String, progress_message::typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), userdata::Nothing, allow_extrapolation::Bool, initialize_integrator::Bool, alias_u0::Bool, alias_du0::Bool, initializealg::OrdinaryDiffEq.DefaultInit, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/vfMzV/src/solve.jl:493
[11] __solve(::ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEFunc
tion{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ::LinearExponential; kwargs::Base.Pairs{Symbol, Vector{Float64}, Tuple{Symbol}, NamedTuple{(:tstops,), Tuple{Vector{Float64}}}}) @ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/vfMzV/src/solve.jl:5
[12] #solve_call#26
@ ~/.julia/packages/DiffEqBase/5rKYk/src/solve.jl:472 [inlined]
[13] #solve_up#32
@ ~/.julia/packages/DiffEqBase/5rKYk/src/solve.jl:834 [inlined]
[14] solve(prob::ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEFu
nction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, args::LinearExponential; sensealg::Nothing, u0::Nothing, p::Nothing, wrap::Val{true}, kwargs::Base.Pairs{Symbol, Vector{Float64}, Tuple{Symbol}, NamedTuple{(:tstops,), Tuple{Vector{Float64}}}}) @ DiffEqBase ~/.julia/packages/DiffEqBase/5rKYk/src/solve.jl:801
[15] fitPKIndLin( __model__ ::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}},
Tuple{}, DynamicPPL.DefaultContext}, __varinfo__ ::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Base.RefValue{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, __context__ ::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random._GLOBAL_RNG}, data::Vector{Float64}) @ Main ~/projects/ghe/pmx-in-julia/TheophPK9.jl:520
[16] macro expansion
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:593 [inlined]
[17] _evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:576 [inlined]
[18] evaluate_threadsafe!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:567 [inlined]
[19] evaluate!!(model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tupl
e{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, context::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random._GLOBAL_RNG}) @ DynamicPPL ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:502
[20] evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:515 [inlined]
[21] evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:523 [inlined]
[22] LogDensityFunction
@ ~/.julia/packages/Turing/szPqN/src/Turing.jl:38 [inlined]
[23] logdensity
@ ~/.julia/packages/Turing/szPqN/src/Turing.jl:42 [inlined]
[24] Fix1
@ ./operators.jl:1096 [inlined]
[25] vector_mode_dual_eval!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/apiutils.jl:37 [inlined]
[26] vector_mode_gradient!(result::DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, f::Ba
se.Fix1{typeof(LogDensityProblems.logdensity), Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}}, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}) @ ForwardDiff ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:113
[27] gradient!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:37 [inlined]
[28] gradient!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:35 [inlined]
[29] logdensity_and_gradient
@ ~/.julia/packages/LogDensityProblems/oAYeE/src/AD_ForwardDiff.jl:49 [inlined]
[30] ∂logπ∂θ
@ ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:166 [inlined]
[31] ∂H∂θ(h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, Base.Fix1{t
ypeof(LogDensityProblems.logdensity), LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}, Turing.Inference.var"#∂logπ∂θ#44"{LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}}, θ::Vector{Float64}) @ AdvancedHMC ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:31
[32] phasepoint
@ ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:76 [inlined]
[33] phasepoint(rng::Random._GLOBAL_RNG, θ::Vector{Float64}, h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuc
lideanMetric{Float64, Vector{Float64}}, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}, Turing.Inference.var"#∂logπ∂θ#44"{LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}}) @ AdvancedHMC ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:153
[34] initialstep(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tu
ple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, vi::DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}; init_params::Nothing, nadapts::Int64, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:170
[35] step(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vec
tor{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}; resume_from::Nothing, init_params::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}}) @ DynamicPPL ~/.julia/packages/DynamicPPL/zPOYL/src/sampler.jl:104
[36] macro expansion
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:120 [inlined]
[37] macro expansion
@ ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:328 [inlined]
[38] macro expansion
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/logging.jl:9 [inlined]
[39] mcmcsample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tup
le{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, discard_initial::Int64, thinning::Int64, chain_type::Type, kwargs::Base.Pairs{Symbol, Union{Nothing, Int64}, Tuple{Symbol, Symbol}, NamedTuple{(:nadapts, :init_params), Tuple{Int64, Nothing}}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:111
[40] #sample#42
@ ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:133 [inlined]
[41] (::AbstractMCMC.var"#sample_chain#78"{String, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTupl
e{(:chain_type, :progress), Tuple{UnionAll, Bool}}}, Random._GLOBAL_RNG, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64})(i::Int64, seed::UInt64, init_params::Nothing) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:506
[42] sample_chain
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:503 [inlined]
[43] #4
@ ./generator.jl:36 [inlined]
[44] iterate
@ ./generator.jl:47 [inlined]
[45] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}}}, Base.var"#4#5
"{AbstractMCMC.var"#sample_chain#78"{String, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTuple{(:chain_type, :progress), Tuple{UnionAll, Bool}}}, Random._GLOBAL_RNG, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64}}}) @ Base ./array.jl:787
[46] map
@ ./abstractarray.jl:3055 [inlined]
[47] mcmcsample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tup
le{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, ::MCMCSerial, N::Int64, nchains::Int64; progressname::String, init_params::Nothing, kwargs::Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTuple{(:chain_type, :progress), Tuple{UnionAll, Bool}}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:518
[48] sample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{V
ector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, ensemble::MCMCSerial, N::Int64, n_chains::Int64; chain_type::Type, progress::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:220
[49] sample
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:209 [inlined]
[50] #sample#6
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:205 [inlined]
[51] sample
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:196 [inlined]
[52] #sample#5
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:192 [inlined]
[53] sample(model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{},
DynamicPPL.DefaultContext}, alg::NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}, ensemble::MCMCSerial, N::Int64, n_chains::Int64) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:184
[54] top-level scope
@ ./timing.jl:262 [inlined]
```
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [October 9, 2022, 3:25pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/6 "2022-10-09T15:25:41Z")
> [@aelmokadem](#):
>
> Was that what you had in mind?
No, you have to actually use that initial condition. `prob = ODEProblem(K, u0, (0.0,24.17))`
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 9, 2022, 6:16pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/7 "2022-10-09T18:16:31Z")
Oh sorry about that. I did try the correct version but still I get the error.
```nohighlight
prob = ODEProblem(K, u0, (0.0,24.17))
```
Could this be related to this issue still? [Error using expv with Turing.jl · Issue #81 · SciML/ExponentialUtilities.jl · GitHub](https://github.com/SciML/ExponentialUtilities.jl/issues/81)?
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [October 9, 2022, 6:30pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/8 "2022-10-09T18:30:50Z")
> [@aelmokadem](#):
>
> Could this be related to this issue still? [Error using expv with Turing.jl · Issue #81 · SciML/ExponentialUtilities.jl · GitHub](https://github.com/SciML/ExponentialUtilities.jl/issues/81)?
No, that was fixed by [add Balance fallback via GenericSchur for non-BlasFloat by ChrisRackauckas · Pull Request #82 · SciML/ExponentialUtilities.jl · GitHub](https://github.com/SciML/ExponentialUtilities.jl/pull/82) and there should be tests covering it in [https://github.com/SciML/ExponentialUtilities.jl/blob/master/test/basictests.jl#L98-L134](https://github.com/SciML/ExponentialUtilities.jl/blob/master/test/basictests.jl#L98-L134) .
Are you sure the error is exactly the same? As in, the stack trace is exactly the same with the same line numbers? Because `A::Vector{Float64}` shouldn’t be a `Vector{Float64}` on that line anymore if `u0` was done correctly: it should match the type of `vec(u0)`. Or maybe a more robust way is `u0 = eltype(K_).([300.0,0.0])`
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 9, 2022, 6:40pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/9 "2022-10-09T18:40:41Z")
You are right. It is a different error message now. The new error message is this
```julia
ERROR: MethodError: no method matching exp(::Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float
64}, Float64, 4}}) Closest candidates are:
exp(::Union{Float16, Float32, Float64}) at special/exp.jl:326
exp(::StridedMatrix{var"#s885"} where var"#s885"<:Union{Float32, Float64, ComplexF32, ComplexF64}) at /App
lications/Julia-1.8.app/Contents/Resources/julia/share/julia/stdlib/v1.8/LinearAlgebra/src/dense.jl:569 exp(::StridedMatrix{var"#s885"} where var"#s885"<:Union{Integer, Complex{<:Integer}}) at /Applications/Jul
ia-1.8.app/Contents/Resources/julia/share/julia/stdlib/v1.8/LinearAlgebra/src/dense.jl:570 ...
Stacktrace:
[1] perform_step!(integrator::OrdinaryDiffEq.ODEIntegrator{LinearExponential, true, Vector{ForwardDiff.Dua
l{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing, Float64, SciMLBase.NullParameters, Float64, Float64, Float64, Float64, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, ODESolution{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, 2, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, ODEProblem{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, LinearExponential, OrdinaryDiffEq.InterpolationData{ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, Vector{Float64}, Vector{Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, OrdinaryDiffEq.LinearExponentialCache{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing}}, DiffEqBase.DEStats}, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, OrdinaryDiffEq.LinearExponentialCache{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing}, OrdinaryDiffEq.DEOptions{Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Float64, Float64, PIController{Int64}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(opnorm), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, Nothing, Nothing, Int64, Vector{Float64}, Tuple{}, Tuple{}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Nothing, OrdinaryDiffEq.DefaultInit}, cache::OrdinaryDiffEq.LinearExponentialCache{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing}, repeat_step::Bool) @ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/vfMzV/src/perform_step/linear_perform_step.jl:735
[2] perform_step!
@ ~/.julia/packages/OrdinaryDiffEq/vfMzV/src/perform_step/linear_perform_step.jl:729 [inlined]
[3] solve!(integrator::OrdinaryDiffEq.ODEIntegrator{LinearExponential, true, Vector{ForwardDiff.Dual{Forwa
rdDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing, Float64, SciMLBase.NullParameters, Float64, Float64, Float64, Float64, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, ODESolution{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, 2, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, ODEProblem{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, LinearExponential, OrdinaryDiffEq.InterpolationData{ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, Vector{Float64}, Vector{Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, OrdinaryDiffEq.LinearExponentialCache{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing}}, DiffEqBase.DEStats}, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, OrdinaryDiffEq.LinearExponentialCache{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing}, OrdinaryDiffEq.DEOptions{Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Float64, Float64, PIController{Int64}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(opnorm), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, Nothing, Nothing, Int64, Vector{Float64}, Tuple{}, Tuple{}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Nothing, OrdinaryDiffEq.DefaultInit}) @ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/vfMzV/src/solve.jl:514
[4] __solve(::ODEProblem{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}},
Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ::LinearExponential; kwargs::Base.Pairs{Symbol, Vector{Float64}, Tuple{Symbol}, NamedTuple{(:tstops,), Tuple{Vector{Float64}}}}) @ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/vfMzV/src/solve.jl:6
[5] #solve_call#26
@ ~/.julia/packages/DiffEqBase/5rKYk/src/solve.jl:472 [inlined]
[6] #solve_up#32
@ ~/.julia/packages/DiffEqBase/5rKYk/src/solve.jl:834 [inlined]
[7] solve(prob::ODEProblem{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}
}, Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, args::LinearExponential; sensealg::Nothing, u0::Nothing, p::Nothing, wrap::Val{true}, kwargs::Base.Pairs{Symbol, Vector{Float64}, Tuple{Symbol}, NamedTuple{(:tstops,), Tuple{Vector{Float64}}}}) @ DiffEqBase ~/.julia/packages/DiffEqBase/5rKYk/src/solve.jl:801
[8] fitPKIndLin( __model__ ::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}},
Tuple{}, DynamicPPL.DefaultContext}, __varinfo__ ::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Base.RefValue{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, __context__ ::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random._GLOBAL_RNG}, data::Vector{Float64}) @ Main ~/projects/ghe/pmx-in-julia/TheophPK9.jl:520
[9] macro expansion
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:593 [inlined]
[10] _evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:576 [inlined]
[11] evaluate_threadsafe!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:567 [inlined]
[12] evaluate!!(model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tupl
e{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, context::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random._GLOBAL_RNG}) @ DynamicPPL ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:502
[13] evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:515 [inlined]
[14] evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:523 [inlined]
[15] LogDensityFunction
@ ~/.julia/packages/Turing/szPqN/src/Turing.jl:38 [inlined]
[16] logdensity
@ ~/.julia/packages/Turing/szPqN/src/Turing.jl:42 [inlined]
[17] Fix1
@ ./operators.jl:1096 [inlined]
[18] vector_mode_dual_eval!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/apiutils.jl:37 [inlined]
[19] vector_mode_gradient!(result::DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, f::Ba
se.Fix1{typeof(LogDensityProblems.logdensity), Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}}, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}) @ ForwardDiff ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:113
[20] gradient!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:37 [inlined]
[21] gradient!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:35 [inlined]
[22] logdensity_and_gradient
@ ~/.julia/packages/LogDensityProblems/oAYeE/src/AD_ForwardDiff.jl:49 [inlined]
[23] ∂logπ∂θ
@ ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:166 [inlined]
[24] ∂H∂θ(h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, Base.Fix1{t
ypeof(LogDensityProblems.logdensity), LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}, Turing.Inference.var"#∂logπ∂θ#44"{LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}}, θ::Vector{Float64}) @ AdvancedHMC ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:31
[25] phasepoint
@ ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:76 [inlined]
[26] phasepoint(rng::Random._GLOBAL_RNG, θ::Vector{Float64}, h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuc
lideanMetric{Float64, Vector{Float64}}, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}, Turing.Inference.var"#∂logπ∂θ#44"{LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}}) @ AdvancedHMC ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:153
[27] initialstep(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tu
ple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, vi::DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}; init_params::Nothing, nadapts::Int64, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:170
[28] step(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vec
tor{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}; resume_from::Nothing, init_params::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}}) @ DynamicPPL ~/.julia/packages/DynamicPPL/zPOYL/src/sampler.jl:104
[29] macro expansion
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:120 [inlined]
[30] macro expansion
@ ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:328 [inlined]
[31] macro expansion
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/logging.jl:9 [inlined]
[32] mcmcsample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tup
le{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, discard_initial::Int64, thinning::Int64, chain_type::Type, kwargs::Base.Pairs{Symbol, Union{Nothing, Int64}, Tuple{Symbol, Symbol}, NamedTuple{(:nadapts, :init_params), Tuple{Int64, Nothing}}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:111
[33] #sample#42
@ ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:133 [inlined]
[34] (::AbstractMCMC.var"#sample_chain#78"{String, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTupl
e{(:chain_type, :progress), Tuple{UnionAll, Bool}}}, Random._GLOBAL_RNG, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64})(i::Int64, seed::UInt64, init_params::Nothing) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:506
[35] sample_chain
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:503 [inlined]
[36] #4
@ ./generator.jl:36 [inlined]
[37] iterate
@ ./generator.jl:47 [inlined]
[38] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}}}, Base.var"#4#5
"{AbstractMCMC.var"#sample_chain#78"{String, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTuple{(:chain_type, :progress), Tuple{UnionAll, Bool}}}, Random._GLOBAL_RNG, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64}}}) @ Base ./array.jl:787
[39] map
@ ./abstractarray.jl:3055 [inlined]
[40] mcmcsample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tup
le{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, ::MCMCSerial, N::Int64, nchains::Int64; progressname::String, init_params::Nothing, kwargs::Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTuple{(:chain_type, :progress), Tuple{UnionAll, Bool}}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:518
[41] sample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{V
ector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, ensemble::MCMCSerial, N::Int64, n_chains::Int64; chain_type::Type, progress::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:220
[42] sample
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:209 [inlined]
[43] #sample#6
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:205 [inlined]
[44] sample
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:196 [inlined]
[45] #sample#5
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:192 [inlined]
[46] sample(model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{},
DynamicPPL.DefaultContext}, alg::NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}, ensemble::MCMCSerial, N::Int64, n_chains::Int64) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:184
[47] top-level scope
@ ./timing.jl:262 [inlined]
[48] top-level scope
@ ~/projects/ghe/pmx-in-julia/TheophPK9.jl:0
```
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 9, 2022, 6:42pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/10 "2022-10-09T18:42:41Z")
Tried `u0 = eltype(K_).([300.0,0.0])` and I get this error message:
```julia
ERROR: MethodError: no method matching exp(::Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float
64}, Float64, 4}}) Closest candidates are:
exp(::Union{Float16, Float32, Float64}) at special/exp.jl:326
exp(::StridedMatrix{var"#s885"} where var"#s885"<:Union{Float32, Float64, ComplexF32, ComplexF64}) at /App
lications/Julia-1.8.app/Contents/Resources/julia/share/julia/stdlib/v1.8/LinearAlgebra/src/dense.jl:569 exp(::StridedMatrix{var"#s885"} where var"#s885"<:Union{Integer, Complex{<:Integer}}) at /Applications/Jul
ia-1.8.app/Contents/Resources/julia/share/julia/stdlib/v1.8/LinearAlgebra/src/dense.jl:570 ...
Stacktrace:
[1] perform_step!(integrator::OrdinaryDiffEq.ODEIntegrator{LinearExponential, true, Vector{ForwardDiff.Dua
l{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing, Float64, SciMLBase.NullParameters, Float64, Float64, Float64, Float64, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, ODESolution{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, 2, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, ODEProblem{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, LinearExponential, OrdinaryDiffEq.InterpolationData{ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, Vector{Float64}, Vector{Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, OrdinaryDiffEq.LinearExponentialCache{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing}}, DiffEqBase.DEStats}, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, OrdinaryDiffEq.LinearExponentialCache{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing}, OrdinaryDiffEq.DEOptions{Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Float64, Float64, PIController{Int64}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(opnorm), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, Nothing, Nothing, Int64, Vector{Float64}, Tuple{}, Tuple{}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Nothing, OrdinaryDiffEq.DefaultInit}, cache::OrdinaryDiffEq.LinearExponentialCache{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing}, repeat_step::Bool) @ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/vfMzV/src/perform_step/linear_perform_step.jl:735
[2] perform_step!
@ ~/.julia/packages/OrdinaryDiffEq/vfMzV/src/perform_step/linear_perform_step.jl:729 [inlined]
[3] solve!(integrator::OrdinaryDiffEq.ODEIntegrator{LinearExponential, true, Vector{ForwardDiff.Dual{Forwa
rdDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing, Float64, SciMLBase.NullParameters, Float64, Float64, Float64, Float64, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, ODESolution{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, 2, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, ODEProblem{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, LinearExponential, OrdinaryDiffEq.InterpolationData{ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}, Vector{Float64}, Vector{Vector{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, OrdinaryDiffEq.LinearExponentialCache{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing}}, DiffEqBase.DEStats}, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, OrdinaryDiffEq.LinearExponentialCache{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Nothing}, OrdinaryDiffEq.DEOptions{Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Float64, Float64, PIController{Int64}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(opnorm), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, Nothing, Nothing, Int64, Vector{Float64}, Tuple{}, Tuple{}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Nothing, OrdinaryDiffEq.DefaultInit}) @ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/vfMzV/src/solve.jl:514
[4] __solve(::ODEProblem{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}},
Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ::LinearExponential; kwargs::Base.Pairs{Symbol, Vector{Float64}, Tuple{Symbol}, NamedTuple{(:tstops,), Tuple{Vector{Float64}}}}) @ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/vfMzV/src/solve.jl:6
[5] #solve_call#26
@ ~/.julia/packages/DiffEqBase/5rKYk/src/solve.jl:472 [inlined]
[6] #solve_up#32
@ ~/.julia/packages/DiffEqBase/5rKYk/src/solve.jl:834 [inlined]
[7] solve(prob::ODEProblem{Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}
}, Tuple{Float64, Float64}, true, SciMLBase.NullParameters, ODEFunction{true, SciMLBase.AutoSpecialize, DiffEqArrayOperator{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, typeof(SciMLBase.DEFAULT_UPDATE_FUNC)}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, args::LinearExponential; sensealg::Nothing, u0::Nothing, p::Nothing, wrap::Val{true}, kwargs::Base.Pairs{Symbol, Vector{Float64}, Tuple{Symbol}, NamedTuple{(:tstops,), Tuple{Vector{Float64}}}}) @ DiffEqBase ~/.julia/packages/DiffEqBase/5rKYk/src/solve.jl:801
[8] fitPKIndLin( __model__ ::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}},
Tuple{}, DynamicPPL.DefaultContext}, __varinfo__ ::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Base.RefValue{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}, __context__ ::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random._GLOBAL_RNG}, data::Vector{Float64}) @ Main ~/projects/ghe/pmx-in-julia/TheophPK9.jl:521
[9] macro expansion
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:593 [inlined]
[10] _evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:576 [inlined]
[11] evaluate_threadsafe!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:567 [inlined]
[12] evaluate!!(model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tupl
e{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}, context::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random._GLOBAL_RNG}) @ DynamicPPL ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:502
[13] evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:515 [inlined]
[14] evaluate!!
@ ~/.julia/packages/DynamicPPL/zPOYL/src/model.jl:523 [inlined]
[15] LogDensityFunction
@ ~/.julia/packages/Turing/szPqN/src/Turing.jl:38 [inlined]
[16] logdensity
@ ~/.julia/packages/Turing/szPqN/src/Turing.jl:42 [inlined]
[17] Fix1
@ ./operators.jl:1096 [inlined]
[18] vector_mode_dual_eval!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/apiutils.jl:37 [inlined]
[19] vector_mode_gradient!(result::DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, f::Ba
se.Fix1{typeof(LogDensityProblems.logdensity), Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}}, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}) @ ForwardDiff ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:113
[20] gradient!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:37 [inlined]
[21] gradient!
@ ~/.julia/packages/ForwardDiff/pDtsf/src/gradient.jl:35 [inlined]
[22] logdensity_and_gradient
@ ~/.julia/packages/LogDensityProblems/oAYeE/src/AD_ForwardDiff.jl:49 [inlined]
[23] ∂logπ∂θ
@ ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:166 [inlined]
[24] ∂H∂θ(h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, Base.Fix1{t
ypeof(LogDensityProblems.logdensity), LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}, Turing.Inference.var"#∂logπ∂θ#44"{LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}}, θ::Vector{Float64}) @ AdvancedHMC ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:31
[25] phasepoint
@ ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:76 [inlined]
[26] phasepoint(rng::Random._GLOBAL_RNG, θ::Vector{Float64}, h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuc
lideanMetric{Float64, Vector{Float64}}, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}, Turing.Inference.var"#∂logπ∂θ#44"{LogDensityProblems.ForwardDiffLogDensity{Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 4}}}}}}) @ AdvancedHMC ~/.julia/packages/AdvancedHMC/iWHPQ/src/hamiltonian.jl:153
[27] initialstep(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tu
ple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, vi::DynamicPPL.TypedVarInfo{NamedTuple{(:σ, :ka, :CL, :V), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{Truncated{Cauchy{Float64}, Continuous, Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ka, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:ka, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:CL, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:CL, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:V, Setfield.IdentityLens}, Int64}, Vector{LogNormal{Float64}}, Vector{AbstractPPL.VarName{:V, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}; init_params::Nothing, nadapts::Int64, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:170
[28] step(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vec
tor{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}; resume_from::Nothing, init_params::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}}) @ DynamicPPL ~/.julia/packages/DynamicPPL/zPOYL/src/sampler.jl:104
[29] macro expansion
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:120 [inlined]
[30] macro expansion
@ ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:328 [inlined]
[31] macro expansion
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/logging.jl:9 [inlined]
[32] mcmcsample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tup
le{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, discard_initial::Int64, thinning::Int64, chain_type::Type, kwargs::Base.Pairs{Symbol, Union{Nothing, Int64}, Tuple{Symbol, Symbol}, NamedTuple{(:nadapts, :init_params), Tuple{Int64, Nothing}}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:111
[33] #sample#42
@ ~/.julia/packages/Turing/szPqN/src/inference/hmc.jl:133 [inlined]
[34] (::AbstractMCMC.var"#sample_chain#78"{String, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTupl
e{(:chain_type, :progress), Tuple{UnionAll, Bool}}}, Random._GLOBAL_RNG, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64})(i::Int64, seed::UInt64, init_params::Nothing) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:506
[35] sample_chain
@ ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:503 [inlined]
[36] #4
@ ./generator.jl:36 [inlined]
[37] iterate
@ ./generator.jl:47 [inlined]
[38] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}}}, Base.var"#4#5
"{AbstractMCMC.var"#sample_chain#78"{String, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTuple{(:chain_type, :progress), Tuple{UnionAll, Bool}}}, Random._GLOBAL_RNG, DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64}}}) @ Base ./array.jl:787
[39] map
@ ./abstractarray.jl:3055 [inlined]
[40] mcmcsample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tup
le{Vector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, ::MCMCSerial, N::Int64, nchains::Int64; progressname::String, init_params::Nothing, kwargs::Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTuple{(:chain_type, :progress), Tuple{UnionAll, Bool}}}) @ AbstractMCMC ~/.julia/packages/AbstractMCMC/fnRmh/src/sample.jl:518
[41] sample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{V
ector{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, ensemble::MCMCSerial, N::Int64, n_chains::Int64; chain_type::Type, progress::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:220
[42] sample
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:209 [inlined]
[43] #sample#6
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:205 [inlined]
[44] sample
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:196 [inlined]
[45] #sample#5
@ ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:192 [inlined]
[46] sample(model::DynamicPPL.Model{typeof(fitPKIndLin), (:data,), (), (), Tuple{Vector{Float64}}, Tuple{},
DynamicPPL.DefaultContext}, alg::NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}, ensemble::MCMCSerial, N::Int64, n_chains::Int64) @ Turing.Inference ~/.julia/packages/Turing/szPqN/src/inference/Inference.jl:184
[47] top-level scope
@ ./timing.jl:262 [inlined]
[48] top-level scope
@ ~/projects/ghe/pmx-in-julia/TheophPK9.jl:0
```
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [October 9, 2022, 7:16pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/11 "2022-10-09T19:16:11Z")
Cool, I was expecting something like that could happen.
>
`exp` isn’t differentiable. That’s the issue. But as mentioned/shown above, the new ones in ExponentialUtilities.jl are. So:
[https://exponentialutilities.sciml.ai/dev/matrix\_exponentials/#Matrix-Exponentials](https://exponentialutilities.sciml.ai/dev/matrix_exponentials/#Matrix-Exponentials)
swap that to `exponential!(dt * Matrix(A))` and it should be good. Give it a quick try?
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 10, 2022, 12:33am UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/12 "2022-10-10T00:33:25Z")
Yup. Will try that and report back how it went.
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 11, 2022, 8:32pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/13 "2022-10-11T20:32:24Z")
That fixed the issue 😆
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 11, 2022, 8:33pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/14 "2022-10-11T20:33:22Z")
@ChrisRackauckas would you like me to set a PR with that fix?
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [October 11, 2022, 8:36pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/15 "2022-10-11T20:36:50Z")
Yes please, and a test that AD of linearexponential works if this change is made.
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 11, 2022, 8:38pm UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/16 "2022-10-11T20:38:12Z")
Sounds good. Thanks a lot @ChrisRackauckas for the help.
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 13, 2022, 12:15am UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/17 "2022-10-13T00:15:39Z")
I forked the OrdinaryDiffEq repo, made the update and added a simple test for AD with `LinearExponential` to the end of `test/interface/ad_tests.jl`. The test code is below. I thought I would run it by you before submitting the PR to see if the test is good enough or if you’d want it somewhere else?! Apologies if it is too naive but this would be my first PR to a julia package.
```julia
# test AD with LinearExponential()
function f(x)
K = DiffEqArrayOperator(x)
u0 = eltype(x).([1.0,0.0])
prob = ODEProblem(K, u0, (0.0,10.0))
sol = solve(prob, LinearExponential(), tstops = [0.0,10.0])[2,:]
end
K_ = [-1.0 0.0; 1.0 -1.0]
@test isapprox(ForwardDiff.jacobian(f,K_)[2], 0.00226999, atol=1e-6)
```
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [October 13, 2022, 5:09am UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/18 "2022-10-13T05:09:06Z")
That’s perfect.
---
### Author:  [@aelmokadem](https://discourse.julialang.org/u/aelmokadem)
#### Post date: [October 13, 2022, 11:39am UTC](https://discourse.julialang.org/t/can-we-use-linearexponential-with-turing-jl-model/88479/19 "2022-10-13T11:39:36Z")
Great. Will submit the PR now 🙂