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	Identifiability and estimation of probabilities from multiple databases with incomplete data and sampling selection
	Jia, Jinzhu ; Geng, Zhi ; Wang, Mingfeng
	2006
关键词	EM ALGORITHM
英文摘要	For an application problem, there may be multiple databases, and each database may not contain complete variables or attributes, that is, some variables are observed but some others are missing. Further, data of a database may be collected conditionally on some designed variables. In this paper, we discuss problems related to data mining from such multiple databases. We propose an approach for detecting identifiability of a joint distribution from multiple databases. For an identifiable joint distribution, we further present the expectation-maximization (EM) algorithm for calculating the maximum likelihood estimates (MLEs) of the joint distribution.; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; SCI(E); CPCI-S(ISTP); 0
语种	英语
出处	SCI
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/314997]
专题	数学科学学院
推荐引用方式 GB/T 7714	Jia, Jinzhu,Geng, Zhi,Wang, Mingfeng. Identifiability and estimation of probabilities from multiple databases with incomplete data and sampling selection. 2006-01-01.

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