An Improved Admissible Heuristic for Finding Optimal Bayesian Networks

Changhe Yuan, Brandon Michael Malone

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Recently two search algorithms, A* and breadth-first branch and bound (BFBnB), were developed based on a simple admissible heuristic for learning Bayesian network structures that optimize a scoring function. The heuristic represents a relaxation of the learning problem such that each variable chooses optimal parents independently. As a result, the heuristic may contain many directed cycles and result in a loose bound. This paper introduces an improved admissible heuristic that tries to avoid directed cycles within small groups of variables. A sparse representation is also introduced to store only the unique optimal parent choices. Empirical results show that the new techniques significantly improved the efficiency and scalability of A* and BFBnB on most of datasets tested in this paper.
Original languageEnglish
Title of host publicationProceedings of the 28th Conference of Uncertainty in Artificial Intelligence
Number of pages10
Publication date2012
Publication statusPublished - 2012
MoE publication typeA4 Article in conference proceedings
EventConference on Uncertainty in Artificial Intelligence - Catalina Island, United States
Duration: 15 Aug 201217 Aug 2012
Conference number: 28

Fields of Science

  • 113 Computer and information sciences

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