Learning Bounded Tree-Width Bayesian Networks via Sampling

Siqi Nie, Cassio P. de Campos, Qiang Ji

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    Abstract

    Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an Informative score function to characterize the quality of a k-tree. The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k. Experiment results indicate that our approach is comparable with exact methods, but is much more computationally efficient.
    Original languageEnglish
    Title of host publicationSymbolic and Quantitative Approaches to Reasoning with Uncertainty
    PublisherSpringer
    Pages387-396
    Number of pages10
    Volume9161
    ISBN (Print)978-3-319-20806-0
    DOIs
    Publication statusPublished - 12 Jul 2015

    Publication series

    NameLecture Notes in Computer Science

    Bibliographical note

    Blind peer reviewed by multiple reviewers.

    Keywords

    • Bayesian network
    • Structure learning
    • Bounded tree-width

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