Hi Ricardo,
Yes, this matches my understanding as well. Thanks for your help!
I was able to achieve the desired behavior by adding an extra dimension to the dirichlet probability matrix. Specifically, I altered the code with this line:
prior_probs = pt.expand_dims(np.ones(shape=(2,2)),1)
to add an extra (flat) dimension in between the variables dimension and the categories dimension.
The approach allows the code to run and produce the desired results. The only downside is that the posterior samples for the “prior” variable also have the extra dimension, but I can work around that.