Presentation	2006-03-15 EM Algorithm for Bayesian Networks using Belief Propagation Yoichi MOTOMURA, Naoki SAKAI,
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Abstract(in English)	Bayesian networks are promising tools for practical probabilistic information processing models using high speed computer and huge data. When we use Bayesian networks constructed from statistical data, missing data is processed by EM algorithm. In this research, EM algorithm for Bayesian networks is discussed. When E-step of EM algorithm, probabilistic reasoning is used on Bayesian networks. We can choose some different reasoning algorithm like (loopy) belief propagation, sampling methods and so on. In this paper, experimental result of EM algorithm using loopy BP and sampling method are shown. We see convergence, speed for some different size of networks.
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Keyword(in English)	Bayesian network / Statistical Learning / Probabilistic reasoning / EM algorithm / Belief propagation
Paper #	NC2005-120
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Conference Information
Committee	NC
Conference Date	2006/3/8(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
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Title (in English)	EM Algorithm for Bayesian Networks using Belief Propagation
Sub Title (in English)
Keyword(1)	Bayesian network
Keyword(2)	Statistical Learning
Keyword(3)	Probabilistic reasoning
Keyword(4)	EM algorithm
Keyword(5)	Belief propagation
1st Author's Name	Yoichi MOTOMURA
1st Author's Affiliation	AIST, Digital Human Research Center()
2nd Author's Name	Naoki SAKAI
2nd Author's Affiliation	Mathematical Systems, Inc.
Date	2006-03-15
Paper #	NC2005-120
Volume (vol)	vol.105
Number (no)	657
Page	pp.pp.-
#Pages	6
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