I have 2 large sets of observations and I would like to do variational inference over the cartesian product of these sets. How do I use
pymc3.Minibatch to get representative samples.
For example suppose the observations a are vactors and I want to model the distribution of dot product of of samples from the 2 sets.
model = Model() with model: A = pm.Minibatch(a, 100) B = pm.Minibatch(b, 100) C = pm.Deterministic('C', A.dot(B)) N = pm.Normal('N, 0, 100, C) fit = pm.fit()
except I think do not think the above will sample fairly from the cartesian product of
How do I do something like the above but sampling uniformly over the cartesian product of