The learning rule for sigmoid belief nets

• Learning is easy if we can get an unbiased sample from the posterior distribution over hidden states given the observed data. • For each unit, maximize the log probability that its binary state in the sample from the posterior would be generated by the sampled binary states of its parents.

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learning rate