Generalized Belief Propagation

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

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Jonathan S. Yedidia, William Freeman, Yair Weiss


Belief propagation (BP) was only supposed to work for tree-like networks but works surprisingly well in many applications involving networks with loops, including turbo codes. However, there has been little understanding of the algorithm or the nature of the solutions it finds for general graphs. We show that BP can only converge to a stationary point of an approximate free energy, known as the Bethe free energy in statis(cid:173) tical physics. This result characterizes BP fixed-points and makes connections with variational approaches to approximate inference. More importantly, our analysis lets us build on the progress made in statistical physics since Bethe's approximation was introduced in 1935. Kikuchi and others have shown how to construct more ac(cid:173) curate free energy approximations, of which Bethe's approximation is the simplest. Exploiting the insights from our analysis, we de(cid:173) rive generalized belief propagation (GBP) versions ofthese Kikuchi approximations. These new message passing algorithms can be significantly more accurate than ordinary BP, at an adjustable in(cid:173) crease in complexity. We illustrate such a new GBP algorithm on a grid Markov network and show that it gives much more accurate marginal probabilities than those found using ordinary BP.