Constraint-based learning Bayesian networks using Bayes factor

Kazuki Natori, Masaki Uto, Yu Nishiyama, Shuichi Kawano, Maomi Ueno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

A score-based learning Bayesian networks, which seeks the best structure with a score function, incurs heavy computational costs. However, a constraint-based (CB) approach relaxes this problem and extends the available learning network size. A severe problem of the CB approach is its lower accuracy of learning than that of a score-based approach. Recently, several CI tests with consistency have been proposed. The main proposal of this study is to apply the CI tests to CB learning Bayesian networks. This method allows learning larger Bayesian networks than the score based approach does. Based on Bayesian theory, this paper addresses a CI test with consistency using Bayes factor. The result shows that Bayes factor with Jeffreys’ prior provides theoretically and empirically best performance.

Original languageEnglish
Title of host publicationAdvanced Methodologies for Bayesian Networks - 2nd International Workshop, AMBN 2015, Proceedings
EditorsJoe Suzuki, Maomi Ueno
PublisherSpringer Verlag
Pages15-31
Number of pages17
ISBN (Print)9783319283784
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event2nd International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015 - Yokohama, Japan
Duration: Nov 16 2015Nov 18 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9505
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015
Country/TerritoryJapan
CityYokohama
Period11/16/1511/18/15

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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