TY - GEN
T1 - Constraint-based learning Bayesian networks using Bayes factor
AU - Natori, Kazuki
AU - Uto, Masaki
AU - Nishiyama, Yu
AU - Kawano, Shuichi
AU - Ueno, Maomi
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84955271429&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84955271429&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-28379-1_2
DO - 10.1007/978-3-319-28379-1_2
M3 - Conference contribution
AN - SCOPUS:84955271429
SN - 9783319283784
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 15
EP - 31
BT - Advanced Methodologies for Bayesian Networks - 2nd International Workshop, AMBN 2015, Proceedings
A2 - Suzuki, Joe
A2 - Ueno, Maomi
PB - Springer Verlag
T2 - 2nd International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015
Y2 - 16 November 2015 through 18 November 2015
ER -