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» Harnessing the Bethe Free Energy
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UAI
2008
14 years 9 days ago
Bounds on the Bethe Free Energy for Gaussian Networks
We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. As an extension of Welling and ...
Botond Cseke, Tom Heskes
UAI
2008
14 years 9 days ago
Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies
Inference problems in graphical models can be represented as a constrained optimization of a free energy function. It is known that when the Bethe free energy is used, the fixedpo...
Tamir Hazan, Amnon Shashua
NIPS
2001
14 years 8 days ago
MIME: Mutual Information Minimization and Entropy Maximization for Bayesian Belief Propagation
Bayesian belief propagation in graphical models has been recently shown to have very close ties to inference methods based in statistical physics. After Yedidia et al. demonstrate...
Anand Rangarajan, Alan L. Yuille
JAIR
2006
143views more  JAIR 2006»
13 years 11 months ago
Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies
Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these algorithms have been...
Tom Heskes
JAIR
2011
114views more  JAIR 2011»
13 years 1 months ago
Properties of Bethe Free Energies and Message Passing in Gaussian Models
We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. We define the Gaussian fracti...
Botond Cseke, Tom Heskes