Learning Bayesian networks for discrete data

Zhang, Jian (2009) Learning Bayesian networks for discrete data. Computational Statistics and Data Analysis, 53 (4). pp. 865-876. ISSN 0167-9473. (doi:10.1016/j.csda.2008.10.007) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)


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.

Item Type: Article
DOI/Identification number: 10.1016/j.csda.2008.10.007
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 11 Oct 2012 17:21 UTC
Last Modified: 29 May 2019 09:34 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/31585 (The current URI for this page, for reference purposes)
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