# Advances in Bayesian Network Learning using Integer Programming

arXiv: https://arxiv.org/abs/1309.6825 · HTML: https://mlsanity.com/paper/1309.6825/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2013.

## Built on by (most cited first, 4 of 4)

- [Active Structure Learning of Bayesian Networks in an Observational Setting](https://mlsanity.com/paper/2103.13796/) (2021)
- [Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings](https://mlsanity.com/paper/2204.02902/) (2022)
- [Scalable Bayesian Network Structure Learning with Splines](https://mlsanity.com/paper/2110.14626/) (2021)
- [Exact Learning Augmented Naive Bayes Classifier](https://mlsanity.com/paper/2107.03018/) (2021)

## 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.