Variational message passing (VMP) is an approximate inference technique for continuous- or discrete-valued Bayesian networks, with conjugate-exponential parents, developed by John Winn. VMP was developed as a means of generalizing the approximate variational methods used by such techniques as Latent Dirichlet allocation and works by updating an approximate distribution at each node through messages in the node's Markov blanket.
Read more about Variational Message Passing: Likelihood Lower Bound, Determining The Update Rule, Messages in Variational Message Passing, Relationship To Exponential Families, VMP Algorithm, Constraints
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