Structure learning of Bayesian networks using constraints

Cassio P. De Campos, Zhi Zeng, Qiang Ji

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

78 Citations (Scopus)


This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful properties that strongly reduce the time and memory costs of many known methods such as hill-climbing, dynamic programming and sampling variable orderings. Secondly, a branch and bound algorithm is presented that integrates parameter and structural constraints with data in a way to guarantee global optimality with respect to the score function. It is an any-time procedure because, if stopped, it provides the best current solution and an estimation about how far it is from the global solution. We show empirically the advantages of the properties and the constraints, and the applicability of the algorithm to large data sets (up to one hundred variables) that cannot be handled by other current methods (limited to around 30 variables).

Original languageEnglish
Title of host publicationProceedings of the 26th Annual International Conference on Machine Learning, ICML'09
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages8
ISBN (Print)978-1-60558-516-1
Publication statusPublished - 9 Dec 2009
Externally publishedYes
Event26th Annual International Conference on Machine Learning, ICML'09 - Montreal, QC, Canada
Duration: 14 Jun 200918 Jun 2009


Conference26th Annual International Conference on Machine Learning, ICML'09
CityMontreal, QC


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