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The problem of learning the structure of Bayesian networks from completediscrete data with a limit on parent set size is considered. Learning is castexplicitly as an optimisation problem where the goal is to find a BN structurewhich maximises log marginal likelihood (BDe score). Integer programming,specifically the SCIP framework, is used to solve this optimisation problem.Acyclicity constraints are added to the integer program (IP) during solving inthe form of cutting planes. Finding good cutting planes is the key to thesuccess of the approach -the search for such cutting planes is effected using asub-IP. Results show that this is a particularly fast method for exact BNlearning.

Bayesian network learning with cutting planes