Integer Programming for Bayesian Network Structure Learning

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)99-110
Number of pages12
JournalQuality Technology and Quantitative Management
Volume11
Issue number1
Publication statusPublished - Mar 2014

Keywords

  • Bayesian networks
  • Integer programming
  • Machine learning

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