nsiccha

Running the code in the [Readme](https://github.com/SciML/DiffEqBayes.jl) gives me a Stan model with two sigma parameters, but a Turing model with a single sigma parameter.

Compare https://github.com/SciML/DiffEqBayes.jl/blob/c983590f6a8e70346dbc52ff6f83a81e0019fafe/src/stan_inference.jl#L58 and https://github.com/SciML/DiffEqBayes.jl/blob/c983590f6a8e70346dbc52ff6f83a81e0019fafe/src/turing_inference.jl#L8 which implies either an InverseGamma(2, 3) or an InverseGamma(3, 3) prior.

Summary of issues with ODE Bayesian parameter estimation benchmarks

18

For now, I'd take the current ODE Bayesian Parameter estimation benchmarks offline and stop quoting them as up-to date evidence that Julia is currently 3-5 times faster than Stan for...

No ESS/s reported for the Bayesian parameter estimation benchmarks

2

N/A

Learn non-linear reparametrization during warm-up

2

It's possible to easily and efficiently learn global non-linear reparametrizations during MCMC warm-up, at a cost comparable to "a few" gradient evaluations of the log prior/jacobian adjustment. The reparametrizations would...

PPC plot for categorical outcomes

2

@n-kall asked me to create an issue concerning the default PPC plot for categorical outcomes, which is less than ideal. Maybe it would be nice to be able to generate...