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A tutorial on variational Bayesian inference
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DOI：10.1007/s10462-011-9236-8.png)
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Abstract
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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.
Keywords:
Variational Bayes
Mean-field
Tutorial
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