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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.

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References

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Authors and Affiliations

  1. Adaptive Behaviour Research Group, University of Sheffield, Sheffield, UK

    Charles W. Fox

  2. Pattern Analysis and Machine Learning Research Group, Department of Engineering Science, University of Oxford, Oxford, UK

    Stephen J. Roberts

Authors
  1. Charles W. Fox
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  2. Stephen J. Roberts
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Correspondence to Charles W. Fox.

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Fox, C.W., Roberts, S.J. A tutorial on variational Bayesian inference. Artif Intell Rev 38, 85–95 (2012). https://doi.org/10.1007/s10462-011-9236-8

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  • DOI: https://doi.org/10.1007/s10462-011-9236-8

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