Thank you for improving Aesara (né: Theano) the last few months. I look forward to trying out the new Jax capabilities.
My model uses the old RandomStreams in the following way:
foo = tt.shared_randomstreams.RandomStreams(seed=seed)
count = foo.binomial(n=total, p=prob)
where total and prob are RVs with big shapes.
This usage was part of the model intended to only be defined for posterior predictive, using @lucianopaz’s model factory idiom. It works fine in PyMC3 3.9.3, but when run in v3.11.0, tt.shared_randomstreams.RandomStreams is no longer found.
Following the relevant documentation, I replaced the above with:
foo = tt.random.utils.RandomStream(seed=seed)
count = foo.binomial(n=total, p=prob)
But that substitution results in a TypeError. It looks like foo.binomial is creating (and sampling from) a BinomialRV, which is not what is needed here. The model just needs a binomial tensor created.
So it appears I need something else rather than tt.random.utils.RandomStream. Or perhaps I am jumping the gun, trying to use PyMC 3.11.0 before it is ready.
Advice?