Three ways to combine probability density models (an underlying theme of the tutorial)
		• Mixture:  Take a weighted average of the distributions. – It can never be sharper than the individual distributions. It’s a very weak way to combine models. • Product: Multiply the distributions at each point and then renormalize. – Exponentially more powerful than a mixture. The normalization makes maximum likelihood learning difficult, but approximations allow us to learn anyway. • Composition: Use the values of the latent variables of one model as the data for the next model. – Works well for learning multiple layers of representation, but only if the individual models are undirected.