Presentation	2004/10/11 Bayesian Network and Probabilistic Inference Iterated Algorithm for Probabilistic Inference Based on Variational Approach Kazuyuki TANAKA,
PDF Download Page	PDF download Page Link
Abstract(in Japanese)	(See Japanese page)
Abstract(in English)	The basic frameworks of the Bayesian network and the belief propagation in the probabilistic inference are reviewed in the standpoint of the variational approach for Kullback-Leibler divergence. In the present tutorial talk, we explain the physical meaning of the belief propagation for probabilistic models denned on graphs with cycles.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	probabilistic information processing / probabilistic inference / belief propagation / Bayesian network / graphical model
Paper #	NC2004-63
Date of Issue

Conference Information
Committee	NC
Conference Date	2004/10/11(1days)
Place (in Japanese)	(See Japanese page)
Place (in English)
Topics (in Japanese)	(See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Bayesian Network and Probabilistic Inference Iterated Algorithm for Probabilistic Inference Based on Variational Approach
Sub Title (in English)
Keyword(1)	probabilistic information processing
Keyword(2)	probabilistic inference
Keyword(3)	belief propagation
Keyword(4)	Bayesian network
Keyword(5)	graphical model
1st Author's Name	Kazuyuki TANAKA
1st Author's Affiliation	Department of Applied Information Sciences, Graduate School of Information Sciences, Tohoku University()
Date	2004/10/11
Paper #	NC2004-63
Volume (vol)	vol.104
Number (no)	348
Page	pp.pp.-
#Pages	8
Date of Issue