An improved structural EM to learn dynamic Bayesian nets

Cassio P. de Campos, Zhi Zeng, Qiang Ji

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

This paper addresses the problem of learning structure of Bayesian and Dynamic Bayesian networks from incomplete data based on the Bayesian Information Criterion. We describe a procedure to map the problem of the dynamic case into a corresponding augmented Bayesian network through the use of structural constraints. Because the algorithm is exact and anytime, it is well suitable for a structural Expectation-Maximization (EM) method where the only source of approximation is due to the EM itself. We show empirically that the use a global maximizer inside the structural EM is computationally feasible and leads to more accurate models.

Original languageEnglish
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages601-604
Number of pages4
ISBN (Electronic)978-1-4244-7541-4
ISBN (Print)978-1-4244-7542-1
DOIs
Publication statusPublished - 18 Nov 2010
Externally publishedYes
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: 23 Aug 201026 Aug 2010

Conference

Conference2010 20th International Conference on Pattern Recognition, ICPR 2010
Country/TerritoryTurkey
CityIstanbul
Period23/08/1026/08/10

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