Hello there,

I am working on a latent variable model with the following form:

Here `p`

is my model, with fixed global parameters `theta`

, while `q`

is my mean-field guide with local variational parameters `phi_i`

.

To speed up learning, I want to perform subsampling on the indexes `i`

, which is very close to stochastic variational inference as presented in http://www.jmlr.org/papers/volume14/hoffman13a/hoffman13a.pdf (except that for them `theta`

is a random variable, corresponding to an additional global term in the guide).

My problem is that I don’t know how to tell Pyro the following: my goal is to learn the global parameter `theta`

, and for this learning phase *I don’t care about the local variational parameters* `phi_i`

. I only want to perform gradient ascent on `theta`

, and compute a batch of `phi_i`

in the background to get a (twice) noisy estimate of the gradient. Is this built in Pyro? Do I have to declare all of the `phi_i`

as parameters nonetheless?

Thanks in advance

Giom

[EDIT] Could it have something to do with this issue: https://github.com/pyro-ppl/pyro/issues/238 ?