I recently started using
pyro. I am working on a problem where I only have un-normalized joint distribution and custom
guide). I would like to maximize ELBO. Here is my question:
Is it possible to provide an example where we have only the unnormalized joint probability of the
model? This can be done pymc3 by defining a distribution object and passing a function that computes
logp. However, defining custom
q is a bit restrictive. Defining custom guide in pyro is straightforward but according to this issue defining a custom probability for the model is not advised. Am I missing something:
If possible, could you please provide an example that one can pass logp and define a custom distribution?