Estimating Dependency Structure as a Hidden Variable

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7245

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DC Field	Value	Language
dc.creator	Meila, Marina	-
dc.creator	Jordan, Michael I.	-
dc.creator	Morris, Quaid	-
dc.date	2004-10-20T21:04:00Z	-
dc.date	2004-10-20T21:04:00Z	-
dc.date	1997-06-01	-
dc.date.accessioned	2013-10-09T02:48:39Z	-
dc.date.available	2013-10-09T02:48:39Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1611	-
dc.identifier	CBCL-151	-
dc.identifier	http://hdl.handle.net/1721.1/7245	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EMand the Minimum Spanning Tree algorithm to find the ML and MAP mixtureof trees for a variety of priors, including the Dirichlet and the MDL priors.	-
dc.format	165004 bytes	-
dc.format	286009 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1611	-
dc.relation	CBCL-151	-
dc.title	Estimating Dependency Structure as a Hidden Variable	-
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