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	Bayesian method for learning graphical models with incompletely categorical data
	Geng, Z ; He, YB ; Wang, XL ; Zhao, Q
	2003
关键词	Bayesian learning graphical model Hyper Markov law incomplete data posterior mean CONTINGENCY-TABLES EXPERT-SYSTEMS MISSING DATA PROBABILITIES
英文摘要	A Bayesian method for learning probabilities under graphical models with incompletely categorical data is discussed, a partial-augmentation formula and a recursive formula of posterior means for graphical models are proposed. Instead of augmenting incompletely observed data to complete data, this partial-augmentation formula only partially augments incomplete data such that augmented data have a monotone data pattern. This recursive formula describes how posterior means are updated by using incomplete data one at a time. Based on this recursive formula, the EM algorithm can be applied to calculate approximate posterior means. This approximate method can be easily applied to Bayesian learning for graphical models. (C) 2003 Elsevier B.V. All rights reserved.; Computer Science, Interdisciplinary Applications; Statistics & Probability; SCI(E); EI; 0; ARTICLE; 1-2; 175-192; 44
语种	英语
出处	EI ; SCI
出版者	computational statistics data analysis
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/158010]
专题	数学科学学院
推荐引用方式 GB/T 7714	Geng, Z,He, YB,Wang, XL,et al. Bayesian method for learning graphical models with incompletely categorical data. 2003-01-01.

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