Forming conditional distributions for inference


#1

I am using Tensorflow to build a VAE which includes image pixels as well as some other continuous variables. The output of the VAE decoder is the following distribution, represented with a tfp distribution:

tfp.distributions.Independent(tfp.distributions.Bernoulli(logits), 2, name=“decoder-dist”)

I am trying to understand how to form other conditional distributions based on this which I can use with the inference methods (MCMC or VI). Say the output above was P(A,B,C | Z), how would I take that distribution to form a posterior P(A|B, C, Z) that I could perform inference on? I have been trying to read through the docs but I am having some trouble grasping them.

I am pretty new to probabilistic programming so its possible I am not even formulating the problem correctly, but I would really appreciate any help!

Thanks