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.

Something like:

```
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 `a`

and `b`

.

How do I do something like the above but sampling uniformly over the cartesian product of `a`

and `b`

?