Turbocharging Treewidth-Bounded Bayesian Network Structure Learning

Authors

  • Vaidyanathan Peruvemba Ramaswamy TU Wien, Vienna, Austria
  • Stefan Szeider TU Wien, Vienna, Austria

Keywords:

Satisfiability, Bayesian Learning, Bayesian Networks

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.

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Published

2021-05-18

How to Cite

Peruvemba Ramaswamy, V., & Szeider, S. (2021). Turbocharging Treewidth-Bounded Bayesian Network Structure Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 3895-3903. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16508

Issue

Section

AAAI Technical Track on Constraint Satisfaction and Optimization