Turbocharging Treewidth-Bounded Bayesian Network Structure Learning

TL;DR

This paper introduces a novel hybrid approach combining exact MaxSAT-based methods with heuristics to efficiently learn large-scale treewidth-bounded Bayesian Network structures, significantly improving their scores.

Contribution

It presents a scalable method that enhances heuristic BN structures with local exact optimization, enabling application to large BNs with thousands of variables.

Findings

Method improves scores of heuristic BNs

Significant improvements over state-of-the-art heuristics

Scales to large BNs with thousands of variables

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.