Integer Linear Programming for the Bayesian network structure learning problem

Bayesian networks are a commonly used method of representing conditional probability relationships between a set of variables in the form of a directed acyclic graph (DAG). Determination of the DAG which best explains observed data is an NP-hard problem [1]. This problem can be stated as a constrain...

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Veröffentlicht in:	Artificial intelligence Jg. 244; S. 258 - 271
Hauptverfasser:	Bartlett, Mark, Cussens, James
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.03.2017 Elsevier Science Ltd
Schlagworte:	Bayesian analysis Bayesian networks Conditional probability Constrained optimisation Cutting planes Economic models Integer Linear Programming Integer programming Linear programming Machine learning Separation Constrained optimisation Separation Bayesian networks Cutting planes Integer Linear Programming
ISSN:	0004-3702, 1872-7921
Online-Zugang:	Volltext
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