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Loopy belief propagation

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If you have a question about this talk, please contact Konstantina Palla.

This talk will consist of two parts. The first will review some fundamental results in the theory of Loopy BP and the second will continue some recent literature on stochastic BP.

In the review part, we’ll talk about the Bethe approximation method (both inference and learning). We will include the equivalence between BP and Bethe approximation from the energy perspective. Related topics in EP and power EP may be discussed if we have time, focusing on energies and divergences.

We will then discuss two recent papers on Stochastic Belief propagation by Noorshams and Wainwright (see below).

This talk is part of the Machine Learning Reading Group @ CUED series.

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