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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. ==Likelihood Lower Bound== Given some set of hidden variables and observed variables , the goal of approximate inference is to lower-bound the probability that a graphical model is in the configuration . Over some probability distribution (to be defined later), :. So, if we define our lower bound to be :, then the likelihood is simply this bound plus the relative entropy between and . Because the relative entropy is non-negative, the function defined above is indeed a lower bound of the log likelihood of our observation . The distribution will have a simpler character than that of because marginalizing over is intractable for all but the simplest of graphical models. In particular, VMP uses a factorized distribution : : where is a disjoint part of the graphical model. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「variational message passing」の詳細全文を読む スポンサード リンク
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