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dc.contributor.authorMeila, Marinaen_US
dc.contributor.authorJordan, Michael I.en_US
dc.contributor.authorMorris, Quaiden_US
dc.date.accessioned2004-10-20T21:04:00Z
dc.date.available2004-10-20T21:04:00Z
dc.date.issued1997-06-01en_US
dc.identifier.otherAIM-1611en_US
dc.identifier.otherCBCL-151en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7245
dc.description.abstractThis 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.en_US
dc.format.extent165004 bytes
dc.format.extent286009 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAIM-1611en_US
dc.relation.ispartofseriesCBCL-151en_US
dc.titleEstimating Dependency Structure as a Hidden Variableen_US


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