🤖 AI Summary
This work proposes a structure compression method for Bayesian networks that preserves the consistency of probabilistic inference while significantly reducing computational complexity. The key innovation lies in introducing a novel combinatorial construct termed the “directed convex hull,” and establishing, for the first time, its equivalence to minimally localized Bayesian networks. Building on this theoretical foundation, the authors design polynomial-time algorithms for constructing and simplifying such structures using directed acyclic graphs. Empirical evaluations on real-world networks demonstrate that the proposed approach substantially improves inference efficiency compared to conventional techniques such as variable elimination and belief propagation. The implementation has been made publicly available as open-source software.
📝 Abstract
This work introduces a novel technique, named structural dimension reduction, to collapse a Bayesian network onto a minimum and localized one while ensuring that probabilistic inferences between the original and reduced networks remain consistent. To this end, we propose a new combinatorial structure in directed acyclic graphs called the directed convex hull, which has turned out to be equivalent to their minimum localized Bayesian networks. An efficient polynomial-time algorithm is devised to identify them by determining the unique directed convex hulls containing the variables of interest from the original networks. Experiments demonstrate that the proposed technique has high dimension reduction capability in real networks, and the efficiency of probabilistic inference based on directed convex hulls can be significantly improved compared with traditional methods such as variable elimination and belief propagation algorithms. The code of this study is open at \href{https://github.com/Balance-H/Algorithms}{https://github.com/Balance-H/Algorithms} and the proofs of the results in the main body are postponed to the appendix.