A tutorial on variational Bayesian inference
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- Fox, C.W. & Roberts, S.J. Artif Intell Rev (2012) 38: 85. doi:10.1007/s10462-011-9236-8
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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.