Getting a sample from the posterior distribution over distributed representations for a given data vector
		• The number of possible hidden configurations is exponential so we need MCMC to sample from the posterior. – It is just the same as getting a sample from the model, except that we keep the visible units clamped to the given data vector. • Only the hidden units are allowed to change states • Samples from the posterior are required for learning the weights.