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A tutorial on variational Bayesian inference

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. © Springer Science+Business Media B.V. 2011.
Publication status:
Published

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Publisher copy:
10.1007/s10462-011-9236-8

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
Journal:
ARTIFICIAL INTELLIGENCE REVIEW
Volume:
38
Issue:
2
Pages:
85-95
Publication date:
2012-08-05
DOI:
EISSN:
1573-7462
ISSN:
0269-2821
URN:
uuid:0a64e4af-a9fd-40a8-8d00-eef52b3a9213
Source identifiers:
350322
Local pid:
pubs:350322
Language:
English
Keywords:

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