, Volume 38, Issue 2, pp 85-95
Date: 15 Jun 2011

A tutorial on variational Bayesian inference

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Abstract

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.