Hello,

I am relatively new to PyTorch and distributions, so forgive any naïveté in the question.

I am attempting to employ PyTorch distributions in a Bayesian inferential framework. At this point I am successfully using Poisson and Gaussian distributions. However, one crucial part of my model/guide is a sort of mapping from `a->a'`

. By itself these two quantities can be arranged in a square 2D matrix with `a`

in rows and `a'`

in columns, as an example. Importantly, this matrix can be given as `P(a'|a)`

in its forward form or `P(a|a')`

in the backwards form (these two are easily computed from each other using Bayes Law).

I am having troubles implementing this `a->a'`

conversion in a way which can facilitate the back-propagation of loss error. In actuality, both `a`

and `a'`

are continuous variables, however, discretization of these two quantities with a integer granularity is reasonable for the purposes of the model.

I have come across the idea of TransformedDistributions, but I am uncertain as to their utility. I have also considered using some combination of Categorical distributions with little success.

Does anyone have any thoughts on how this could be achieved?

Thanks for any hints/tips/tricks!