Gibbs sampling
	• First fix a datavector from the training set on the visible units. • Then keep visiting hidden units and updating their binary states using information from their parents and descendants. • If we do this in the right way, we will eventually get unbiased samples from the posterior distribution for that datavector. • This is relatively efficient because almost all hidden configurations will have negligible probability and will probably not be visited.