Bayesian network learning with cutting planes

Abstract

The problem of learning the structure ofBayesian networks from complete discretedata with a limit on parent set size is consid-ered. Learning is cast explicitly as an optimi-sation problem where the goal is to find a BNstructure which maximises log marginal like-lihood (BDe score). Integer programming,specifically the SCIP framework, is used tosolve this optimisation problem. Acyclic-ity constraints are added to the integer pro-gram (IP) during solving in the form of cut-ting planes. Finding good cutting planes isthe key to the success of the approach—thesearch for such cutting planes is effected usinga sub-IP. Results show that this is a particu-larly fast method for exact BN learning