Learning Deep Belief Nets
	• It is easy to generate an unbiased example at the leaf nodes, so we can see what kinds of data the network believes in. • It is hard to infer the posterior distribution over all  possible configurations of hidden causes. • It is hard to even get  a sample from the posterior. • So how can we learn deep belief nets that have millions of parameters?
							stochastic hidden cause
								visible effect