Turbocharging Treewidth-Bounded Bayesian Network Structure Learning
Artificial Intelligence
2021-02-08 v2 Machine Learning
Abstract
We present a new approach for learning the structure of a treewidth-bounded Bayesian Network (BN). The key to our approach is applying an exact method (based on MaxSAT) locally, to improve the score of a heuristically computed BN. This approach allows us to scale the power of exact methods -- so far only applicable to BNs with several dozens of random variables -- to large BNs with several thousands of random variables. Our experiments show that our method improves the score of BNs provided by state-of-the-art heuristic methods, often significantly.
Cite
@article{arxiv.2006.13843,
title = {Turbocharging Treewidth-Bounded Bayesian Network Structure Learning},
author = {Vaidyanathan P. R. and Stefan Szeider},
journal= {arXiv preprint arXiv:2006.13843},
year = {2021}
}
Comments
15 pages, 4 figures, 3 tables. To be published in AAAI 2021. Updated: synced with AAAI version. Source code available at http://github.com/aditya95sriram/bn-slim