説明
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/