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Artificial Intelligence Review In Artificial Intelligence Review, Vol. 38, No. 2. (1 August 2012), pp. 85-95, doi:10.1007/s10462-011-9236-8 Key: citeulike:9432482
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This tutorial describes the mean-field variational Bayesian approximation to inference in graphical models, using modern machine learning terminology rather than statistical physics concepts. It begins by seeking to find an approximate mean-field distribution close to the target joint in the KL-divergence sense. It then derives local node updates and reviews the recent Variational Message Passing framework.
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