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Abstract: We present a class of generalized mean eld (GMF) algorithms for approximate inference in exponential family graphical models which is analogous to the generalized belief propagation (GBP) or cluster variational methods. (Update)
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BibTeX entry: (Update)
@misc{ for-generalized,
author = "Eld Algorithm For",
title = "A Generalized Mean",
url = "citeseer.ist.psu.edu/751746.html" }
Citations (may not include all citations):461 Markov Chain Monte Carlo in Practice (context) - Gilks, Richardson et al. - 1996
245 An introduction to variational methods for graphical models - Jordan, Ghahramani et al. - 1999
151 Factorial hidden markov models - Ghahramani, Jordan - 1997
102 Non-universal critical dynamics in Monte Carlo simulation (context) - Swendsen, Wang - 1987
88 Propagation algorithms for variational Bayesian learning - Ghahramani, Beal - 2000
68 Exploiting tractable substructures in intractable networks - Saul, Jordan - 1996
46 Understanding belief propagation and its generalizations (context) - Yedidia, Freeman et al. - 2001
25 Expectation propagation for approximate Bayesian inference (context) - Minka - 2001
10 Bayesian logistic regression: A variational approach (context) - Jaakkola, Jordan - 2000
9 A tighter bound for graphical models - Leisink, Kappen - 2000
6 VIBES: A variational inference engine for Bayesian networks (context) - Bishop, Spiegelhalter et al. - 2002
3 Variational approximations between mean eld theory and the j.. (context) - Wiegerinck - 2000
1 Incorporating expressive graphical models in variational app.. (context) - El-Hay, Friedman - 2001
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