This Wednesday I'll pick up where we left off last week when we covered graphical models, exponential families, and the basic ideas behind variational inference. This week I will go over variational inference in greater depth, and then describe some approximations to the variational problem that render it tractable: sum-product and the Bethe entropy approximation; mean field methods (time permitting); and convex approximations, in particular tree-reweighted belief propagation. The material is drawn from chapters 3, 4, 5, and 7 of the same paper.
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