An analogy
	• In a mixture model, we define the probability of a datavector to be • The learning rule for the mixing proportions is to make them match the posterior probability of using each Gaussian. • The weights of an RBM implicitly define a mixing proportion for each possible hidden vector. – To fit the data better, we can leave p(v|h) the same and make the mixing proportion of each hidden vector more like the posterior over hidden vectors.