Integer Programming for Bayesian Network Structure Learning

Bayesian networks provide an attractive representation of structured probabilistic information. There is thus much interest in 'learning' BNs from data. In this paper the problem of learning a Bayesian network using integer programming is presented. The SCIP (Solving Constraint Integer Pro...

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Veröffentlicht in:Quality technology & quantitative management Jg. 11; H. 1; S. 99 - 110
1. Verfasser: Cussens, James
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Taylor & Francis 2014
Schlagworte:
Bayesian networks
integer programming
machine learning
ISSN:1684-3703, 1684-3703
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Zusammenfassung:Bayesian networks provide an attractive representation of structured probabilistic information. There is thus much interest in 'learning' BNs from data. In this paper the problem of learning a Bayesian network using integer programming is presented. The SCIP (Solving Constraint Integer Programming) framework is used to do this. Although cutting planes are a key ingredient in our approach, primal heuristics and efficient propagation are also important.
ISSN:1684-3703
1684-3703
DOI:10.1080/16843703.2014.11673328