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The mean field variational Bayes method is becoming increasingly popular in statistics and machine learning.
Stochastic blockmodels: First steps
Paul W Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt · 1983
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Sampling-based approaches to calculating marginal densities
Alan E Gelfand and Adrian FM Smith · 1990
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An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
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Beatrice Laurent and Pascal Massart · 2000
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David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
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Monte carlo methods
Christian P Robert · 2004
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William D Penny, Nelson J Trujillo-Barreto, and Karl J Friston · 2005
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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Convergence properties of a general algorithm for calculating variational bayesian estimates for a normal mixture model
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Jake M Hofman and Chris H Wiggins · 2008
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Martin J Wainwright and Michael I Jordan · 2008
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Peter J Bickel and Aiyou Chen · 2009
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Karl Rohe, Sourav Chatterjee, and Bin Yu · 2011
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Ted Westling and Tyler H McCormick · 2015
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Community detection in sparse networks via Grothendieck’s inequality
Olivier Guédon and Roman Vershynin · 2016
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Statistical and computational guarantees of Lloyd’s algorithm and its variants
Yu Lu and Harrison H Zhou · 2016
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Minimax rates of community detection in stochastic block models
Anderson Y Zhang and Harrison H Zhou · 2016
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Supplement to “theoretical and computational guarantees of mean field variational inference for community detection”
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