The expression of conditional prior and y-prior terms in the code.

Question

The expression of conditional prior and y-prior terms in the code.

Closed this issue 5 years ago · 3 comments

Hello, thanks for sharing. I've read the original paper about GMVAE. But I still don't know how to derive the conditional prior and y-prior terms as you did in the code. Can you explain more about the derivation?

Answer 1 · 2019-02-14T10:54:19.000Z

Hi. In Section 4 of my Bachelor's dissertation you may find a description of the model proposed by Nat Dilokthanakul et al. Please, notice the naming of the variables we use differs from the one of the original paper. Then, in Appendix D of the dissertation you may find the full derivation of all the terms of the ELBO. Hope this helps!

Answer 2 · 2019-02-14T13:29:09.000Z

Thanks a lot for your reply. That helps much. And what is the "log det" when simplify the terms D.1 in the Appendix D?

Answer 3 · 2019-02-14T14:15:29.000Z

It refers to the log of the determinant of the covariance matrix (represented as sigma squared). Since we consider a mean field approximation for the distributions, the determinant of the covariance matrix is just the product of the terms in the diagonal. Hope this helps!