Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Variational message passing 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. Because every child must be conjugate to its parent, this limits the types of distributions that can be used in the model. For example, the parents of a Gaussian distribution must be a Gaussian distribution and a gamma distribution.
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