Estimating Dependency Structure as a Hidden Variable
| dc.date.accessioned | 2004-10-20T21:04:00Z | |
| dc.date.accessioned | 2018-11-24T10:23:30Z | |
| dc.date.available | 2004-10-20T21:04:00Z | |
| dc.date.available | 2018-11-24T10:23:30Z | |
| dc.date.issued | 1997-06-01 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/7245 | |
| dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/7245 | |
| dc.description.abstract | 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. | en_US |
| dc.format.extent | 165004 bytes | |
| dc.format.extent | 286009 bytes | |
| dc.language.iso | en_US | |
| dc.title | Estimating Dependency Structure as a Hidden Variable | en_US |
Files in this item
| Files | Size | Format | View |
|---|---|---|---|
| AIM-1611.pdf | 286.0Kb | application/pdf | View/ |
| AIM-1611.ps | 165.0Kb | application/postscript | View/ |