Bayesian variable (one-object mode)

I think we could wrap our mcmc parameters in a custom class that would make it easier to work with the mcmc results (in the spirit of rval (?) in posterior package).

In mcmc world (samples)

Scalar -> shape=chain,draw Vector -> shape=chain,draw,vector_dim Matrix -> shape=chain,draw,*matrix_dims

example

When we want to do matrix * vector product with mcmc results, we need to be careful that correct dimensions are used -> Bayesian variable could handle this so the variable would work as any non-mcmc variable.

repr

We don’t always need to show all the samples for users but it might be better show some specific statistics (e.g. mean, std)

Our html output could also have other info, rhat/ess (maybe even density picture?)

Similar work can be seen in https://pythonhosted.org/uncertainties/

[grburgess]

I like this idea. I’ve worked on similar things in the past and will be willing to help.

[OriolAbril]

I have written down and organized everything I had scattered around and tried to make the library somewhat coherent. Somehow I had forgotten about this issue until today.

Here is the link: https://xarray-einstats.readthedocs.io/en/latest/. It has linear algebra wrappers from numpy, rv and summary wrappers from scipy.stats, a couple wrappers for einops (ignoring coord values) and a super cool (if I may say so myself 😎) histogram that bins in a vectorized way along any given dimensions (powered by numba).

I am currently updating some examples from the pymc collection to use xarray-einstats to test it a bit more and iron it out, then make a 0.2 release and start advertising it more broadly. Feature requests, suggestions and collaborations always welcome whatever the release state though