Handle URI:
http://hdl.handle.net/10754/598713
Title:
Learning Bayesian networks for discrete data
Authors:
Liang, Faming; Zhang, Jian
Abstract:
Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.
Citation:
Liang F, Zhang J (2009) Learning Bayesian networks for discrete data. Computational Statistics & Data Analysis 53: 865–876. Available: http://dx.doi.org/10.1016/j.csda.2008.10.007.
Publisher:
Elsevier BV
Journal:
Computational Statistics & Data Analysis
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Feb-2009
DOI:
10.1016/j.csda.2008.10.007
Type:
Article
ISSN:
0167-9473
Sponsors:
Liang's research was supported in part by the grant (DMS-0607755) of the National Science Foundation and the award (KUS-C1-016-04) given by King Abdullah University of Science and Technology (KAUST). The authors thank Professor S.P. Azen, the associate editor, and the referee for their comments which have led to significant improvement of this paper.
Appears in Collections:
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Full metadata record

DC FieldValue Language
dc.contributor.authorLiang, Famingen
dc.contributor.authorZhang, Jianen
dc.date.accessioned2016-02-25T13:34:55Zen
dc.date.available2016-02-25T13:34:55Zen
dc.date.issued2009-02en
dc.identifier.citationLiang F, Zhang J (2009) Learning Bayesian networks for discrete data. Computational Statistics & Data Analysis 53: 865–876. Available: http://dx.doi.org/10.1016/j.csda.2008.10.007.en
dc.identifier.issn0167-9473en
dc.identifier.doi10.1016/j.csda.2008.10.007en
dc.identifier.urihttp://hdl.handle.net/10754/598713en
dc.description.abstractBayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.en
dc.description.sponsorshipLiang's research was supported in part by the grant (DMS-0607755) of the National Science Foundation and the award (KUS-C1-016-04) given by King Abdullah University of Science and Technology (KAUST). The authors thank Professor S.P. Azen, the associate editor, and the referee for their comments which have led to significant improvement of this paper.en
dc.publisherElsevier BVen
dc.titleLearning Bayesian networks for discrete dataen
dc.typeArticleen
dc.identifier.journalComputational Statistics & Data Analysisen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionUniversity of York, York, United Kingdomen
kaust.grant.numberKUS-C1-016-04en
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