Estimating Dependency Structure as a Hidden Variable

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

Files in this item

Files	Size	Format	View
There are no files associated with this item.