A tutorial on variational Bayesian inference

A tutorial on variational Bayesian inference

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-dive...

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Veröffentlicht in:The Artificial intelligence review 2012-08, Vol.38 (2), p.85-95
Hauptverfasser: Fox, Charles W., Roberts, Stephen J.
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Approximation
Artificial Intelligence
Computer Science
Energy
Machine learning
Variables
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Beschreibung
Zusammenfassung: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.
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-011-9236-8