Why EM converges
	• There is a cost function that is reduced by both the E-step and the M-step.                  Cost  =  expected energy  –  entropy • The expected energy term measures how difficult it is to generate each datapoint from the Gaussians it is assigned to. It would be happiest giving all the responsibility for each datapoint to the most likely Gaussian (as in K-means). • The entropy term encourages “soft” assignments. It would be happiest spreading the responsibility for each datapoint equally between all the Gaussians.