## 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.

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 language | English |
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Title of host publication | Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013) |

Publisher | AUAI Press |

Pages | 182-191 |

Number of pages | 10 |

Publication status | Published - 2013 |