Add additional distributions to Deep evidential regression

[DoktorMike]

Currently we assume that all regression cases will want to use a y~Normal(mu, sigma) model. This is definitely not always true and there may be many other distributions which could be useful for a deep learning setting. For instance.

• Poisson
• Binomial
• NegativeBinomial
• StudentT (This might be a bit flaky since we do end up with a model evidence following a StudentT either way)
• Cauchy