Updates

Author: ParamThakkar123

benchmarks/BayesianInference/DiffEqBayesFitzHughNagumo.jmd

@@ -64,7 +64,7 @@ priors = [truncated(Normal(1.0,0.5),0,1.5), truncated(Normal(1.0,0.5),0,1.5), tr
 #### Stan.jl backend
 
 ```julia
-@time bayesian_result_stan = stan_inference(prob_ode_fitzhughnagumo, :rk45, t, data, priors, nothing, Normal, vars=(DiffEqBayes.StanODEData(), InverseGamma(2, 3)), sample_u0=false, solve_kwargs=Dict(), diffeq_string=nothing, sample_kwargs=Dict(), output_format=:mcmcchains, print_summary=false, tmpdir=mktempdir())
+@time bayesian_result_stan = stan_inference(prob_ode_fitzhughnagumo, :rk45, t, data, priors, nothing, Normal, vars=(DiffEqBayes.StanODEData(), InverseGamma(2, 3)), sample_u0=false, diffeq_string=nothing, output_format=:mcmcchains, print_summary=false, tmpdir=mktempdir())
 ```
 
 ### Direct Turing.jl

benchmarks/BayesianInference/DiffEqBayesLorenz.jmd

@@ -95,7 +95,7 @@ priors = [truncated(Normal(10,2),1,15),truncated(Normal(30,5),1,45),truncated(No
 Lorenz equation is a chaotic system hence requires very low tolerance to be estimated in a reasonable way, we use 1e-8 obtained from the uncertainty plots. Use of truncated priors is necessary to prevent Stan from stepping into negative and other improbable areas.
 
 ```julia
-@time bayesian_result_stan = stan_inference(prob, :rk45, t, data, priors, nothing, Normal, vars=(DiffEqBayes.StanODEData(), InverseGamma(2, 3)), sample_u0=false, solve_kwargs=Dict(), diffeq_string=nothing, sample_kwargs=Dict(), output_format=:mcmcchains, print_summary=false, tmpdir=mktempdir())
+@time bayesian_result_stan = stan_inference(prob, :rk45, t, data, priors, nothing, Normal, vars=(DiffEqBayes.StanODEData(), InverseGamma(2, 3)), sample_u0=false, diffeq_string=nothing, output_format=:mcmcchains, print_summary=false, tmpdir=mktempdir())
 ```
 
 ### Direct Turing.jl

benchmarks/BayesianInference/DiffEqBayesLotkaVolterra.jmd

@@ -71,7 +71,7 @@ priors = [truncated(Normal(1.5,0.5),0.5,2.5),truncated(Normal(1.2,0.5),0,2),trun
 The solution converges for tolerance values lower than 1e-3, lower tolerance leads to better accuracy in result but is accompanied by longer warmup and sampling time, truncated normal priors are used for preventing Stan from stepping into negative values.
 
 ```julia
-@btime bayesian_result_stan = stan_inference(prob,:rk45, t, data, priors, nothing, Normal, vars=(DiffEqBayes.StanODEData(), InverseGamma(2, 3)), sample_u0=false, solve_kwargs=Dict(), diffeq_string=nothing, sample_kwargs=Dict(), output_format=:mcmcchains, print_summary=false, tmpdir=mktempdir())
+@btime bayesian_result_stan = stan_inference(prob,:rk45, t, data, priors, nothing, Normal, vars=(DiffEqBayes.StanODEData(), InverseGamma(2, 3)), sample_u0=false, diffeq_string=nothing, output_format=:mcmcchains, print_summary=false, tmpdir=mktempdir())
 ```
 
 ### Direct Turing.jl