Advances in Bayesian Network Learning using Integer Programming

Research output: Other contribution


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.
Original languageEnglish
PublisherAUAI Press
Number of pages10
Publication statusPublished - 2013


Dive into the research topics of 'Advances in Bayesian Network Learning using Integer Programming'. Together they form a unique fingerprint.

Cite this