Finding All Bayesian Network Structures within a Factor of Optimal

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Publication details

Title of host publicationProceedings of the AAAI Conference on Artificial Intelligence, 33(01)
DateAccepted/In press - 10 Dec 2018
DatePublished (current) - 17 Jul 2019
Number of pages8
PublisherAAAI Press
Original languageEnglish
ISBN (Print)978-1-57735-809-1

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468


A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known score-andsearch approach. However, selecting a single model (i.e., the best scoring BN) can be misleading or may not achieve the best possible accuracy. An alternative to committing to a single model is to perform some form of Bayesian or frequentist model averaging, where the space of possible BNs is sampled or enumerated in some fashion. Unfortunately, existing approaches for model averaging either severely restrict the structure of the Bayesian network or have only been shown to scale to networks with fewer than 30 random variables. In
this paper, we propose a novel approach to model averaging inspired by performance guarantees in approximation algorithms. Our approach has two primary advantages. First, our approach only considers credible models in that they are optimal or near-optimal in score. Second, our approach is more
efficient and scales to significantly larger Bayesian networks than existing approaches.

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