





Each hidden unit
has a binary state which is 1 if the unigauss chose its


Gaussian. Start
with a training vector on the visible units. Then alternate


between updating
all the hidden units in parallel and updating all the


visible units in parallel.



Update
the hidden states by picking from the posterior.



Update
the visible states by picking from the Gaussian you get when you

multiply
together all the Gaussians for the active hidden units.

