Belief propagation, or sum-product message passing, is an algorithm for inferring probabilities from graphical models, such as Bayesian networks and Markov random fields.
The LoopyBeliefPropagation function calculates, for a Bayesian network of binary variables, the marginal distribution for each unobserved variable, conditional on any observed variables.