What is easy and what is hard in a DAG?
		• It is easy to generate an unbiased example at the leaf nodes. • It is typically hard to compute the posterior distribution over all possible configurations of hidden causes. It is also hard to compute the probability of an observed vector. • Given samples from the posterior, it is easy to learn the conditional probabilities that define the model.
					Hidden cause
						Visible effect