Hi everyone, I have a strangely specific question. Is there a way to evaluate pointwise the joint probability density function (lets assume all my latents are continuous) and particularly its gradient on the constrained (i.e., not reparametrized) space? I know most of the Pyro internals for MCMC and SVI reparametrize the underlying space first so everything happens in R^d, but for a current research project I would like to use Pyro for, I need to work directly on the constrained space. Any tips would be appreciated!
@Robsal In Pyro, the joint probability density is in constrained space. You can get it with
In mcmc and autoguides, we generate samples in unconstrained space, so we will adjust some log_abs_det_jacobian term to correct it. You don’t have to worry about it here I guess.