Bayesian network structure learning with integer programming: Polytopes, facets and complexity

James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett

Research output: Contribution to conferenceAbstractScientificpeer-review

1 Citation (Scopus)


Developing accurate algorithms for learning structures of probabilistic graphical models is an important problem within modern AI research. Here we focus on score-based structure learning for Bayesian networks as arguably the most central class of graphical models. A successful generic approach to optimal Bayesian network structure learning (BNSL), based on integer programming (IP), is implemented in the GOBNILP system. Despite the recent algorithmic advances, current understanding of foundational aspects underlying the IP based approach to BNSL is still somewhat lacking. In this paper, we provide theoretical contributions towards understanding fundamental aspects of cutting planes and the related separation problem in this context, ranging from NP-hardness results to analysis of polytopes and the related facets in connection to BNSL.

Original languageEnglish
Number of pages5
Publication statusPublished - 1 Jan 2017
MoE publication typeNot Eligible
EventInternational Joint Conference on Artificial Intelligence - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017
Conference number: 26


ConferenceInternational Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI
Internet address

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