Advances in Bayesian Network Learning using Integer Programming
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2013.
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Citing papers per year: 2021: 3; 2022: 1.
4 papers.
- 2021Active Structure Learning of Bayesian Networks in an Observational Settingcited by 4
- 2022Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderingscited by 3
- 2021Scalable Bayesian Network Structure Learning with Splinescited by 2
- 2021Exact Learning Augmented Naive Bayes Classifiercited by 0
- 2022Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings6 Apr 2022
- 2021Scalable Bayesian Network Structure Learning with Splines27 Oct 2021
- 2021Exact Learning Augmented Naive Bayes Classifier7 Jul 2021
- 2021Active Structure Learning of Bayesian Networks in an Observational Setting25 Mar 2021