Belief propagation is an algorithm used in probabilistic graphical models to efficiently compute marginal distributions of a subset of variables given some observed data. This technique leverages the structure of the graph to update and propagate beliefs about the state of variables throughout the network, making it particularly useful in scenarios like inference in Bayesian networks and Markov random fields. By systematically passing messages between nodes, belief propagation helps simplify complex probability calculations and draws on the relationships between variables.
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