α Belief Propagation for Approximate Inference

Code

• github.com/FirstHandScientist/AlphaBP
  OfficialIn papernone★ 1

Abstract

Belief propagation (BP) algorithm is a widely used message-passing method for inference in graphical models. BP on loop-free graphs converges in linear time. But for graphs with loops, BP's performance is uncertain, and the understanding of its solution is limited. To gain a better understanding of BP in general graphs, we derive an interpretable belief propagation algorithm that is motivated by minimization of a localized -divergence. We term this algorithm as belief propagation (-BP). It turns out that -BP generalizes standard BP. In addition, this work studies the convergence properties of -BP. We prove and offer the convergence conditions for -BP. Experimental simulations on random graphs validate our theoretical results. The application of -BP to practical problems is also demonstrated.

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