Learning Bayesian networks for discrete data

Faming Liang, Jian Zhang

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)865-876
Number of pages12
JournalComputational Statistics & Data Analysis
Volume53
Issue number4
DOIs
StatePublished - Feb 2009
Externally publishedYes

Fingerprint

Dive into the research topics of 'Learning Bayesian networks for discrete data'. Together they form a unique fingerprint.

Cite this