Belief propagation is a message passing algorithm for performing inference on graphical models, such as Bayesian networks and Markov random fields. It is an inherently Bayesian procedure, which calculates the marginal distribution for each unobserved node, conditional on any observed nodes. Belief propagation is commonly used in artificial intelligence and information theory and has demonstrated empirical success in numerous applications including low-density parity-check codes, turbo codes, free energy approximation, and satisfiability.

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