Advances in Learning Bayesian Networks of Bounded Treewidth
Abstract
This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.
Cite
Text
Nie et al. "Advances in Learning Bayesian Networks of Bounded Treewidth." Neural Information Processing Systems, 2014.Markdown
[Nie et al. "Advances in Learning Bayesian Networks of Bounded Treewidth." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/nie2014neurips-advances/)BibTeX
@inproceedings{nie2014neurips-advances,
title = {{Advances in Learning Bayesian Networks of Bounded Treewidth}},
author = {Nie, Siqi and Maua, Denis D. and de Campos, Cassio P and Ji, Qiang},
booktitle = {Neural Information Processing Systems},
year = {2014},
pages = {2285-2293},
url = {https://mlanthology.org/neurips/2014/nie2014neurips-advances/}
}