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Variational Approximations for Inference over Graphical Models
Andrés Corrada-Emmanuel
UMass
Abstract
This is a tutorial talk on the use of variational approximations for inference calculations over graphical models. Variational methods connect "integral" and "differential" viewpoints of physical systems. They also provide a framework for developing approximations to the marginal and posterior probabilities that are needed for inference calculations over graphical models. I'll discuss a variational reworking of the Expectation Maximization (EM) algorithm, "mean field" approximations, and the equivalence between the Belief Propagation (BP) algorithm and the Bethe approximation for the 2-D Ising model.
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