The learning rule for sigmoid belief nets
Suppose we could observe
the states of all the hidden
units when the net was
generating an observed data-
This is equivalent to getting
samples from the posterior
distribution over hidden
configurations given the
observed datavactor.
For each node, it is easy to
maximize the log probability of
its observed state given the
observed states of its parents.
probability of i
turning on given the
states of its parents