Advances in Bayesian Network Learning using Integer Programming

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Title of host publicationProceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013)
DatePublished - 2013
Pages182-191
Number of pages10
PublisherAUAI Press
Original languageEnglish

Abstract

We consider the problem of learning Bayesian networks (BNs) from
complete discrete data. This problem of discrete optimisation is
formulated as an integer program (IP). We describe the various steps
we have taken to allow efficient solving of this IP. These are (i)
efficient search for cutting planes, (ii) a fast greedy algorithm to
find high-scoring (perhaps not optimal) BNs and (iii) tightening
the linear relaxation of the IP. After relating this BN
learning problem to set covering and the multidimensional 0-1
knapsack problem, we present our empirical results. These show
improvements, sometimes dramatic, over earlier results.

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