Hi - I’m using an
AutoGuide, specifically an
AutoDelta guide, and am trying to constrain its parameters. In particular, I want the posterior mean for a particular latent variable to be positive.
In an earlier version of Pyro, one could apparently do this manually, prior to inference, by prepending inference with lines like below.
pyro.param("auto_concentration", torch.ones(k), constraint=constraints.positive)
Is it still possible to constrain your
AutoGuides parameters? Is it better to apply a function yourself with known range to the result of a sampling statement in your model, e.g. softplus or the exponential function?