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 , t, data, priors; num_samples = 10_000, vars=(StanODEData(), InverseGamma(2, 3)))
+@time bayesian_result_stan = stan_inference(prob_ode_fitzhughnagumo,t,data,priors; delta = 0.65, num_samples = 10_000, print_summary=false, vars=(DiffEqBayes.StanODEData(), InverseGamma(2, 3)))
 ```
 
 ### Direct Turing.jl
@@ -112,4 +112,4 @@ FitzHugh-Ngumo is a standard problem for parameter estimation studies. In the Fi
 ```julia, echo = false
 using SciMLBenchmarks
 SciMLBenchmarks.bench_footer(WEAVE_ARGS[:folder],WEAVE_ARGS[:file])
-```
+```

benchmarks/BayesianInference/DiffEqBayesLorenz.jmd

@@ -148,4 +148,4 @@ Its uncertainty plot points to its chaotic behaviour and goes awry for different
 ```julia, echo = false
 using SciMLBenchmarks
 SciMLBenchmarks.bench_footer(WEAVE_ARGS[:folder],WEAVE_ARGS[:file])
-```
+```

benchmarks/BayesianInference/DiffEqBayesLotkaVolterra.jmd

@@ -126,4 +126,4 @@ It depicts a cyclic behaviour, which is also seen in its Uncertainty Quantificat
 ```julia, echo = false
 using SciMLBenchmarks
 SciMLBenchmarks.bench_footer(WEAVE_ARGS[:folder],WEAVE_ARGS[:file])
-```
+```