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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- vector. – 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.

j

i

probability of i turning on given the states of its parents