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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)

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http://dx.doi.org/10.1016/j.csda.2008.10.007

Abstract

Bayesian networks have received much attention in the recent literature. In this article,

Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses

by conventional MCMC simulation-based approaches in learning Bayesian networks.

features can be inferred by dynamically weighted averaging the samples generated in the

model-based estimates. The numerical results indicate that our approach can mix much

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: 13 Feb 2020 04:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/31585 (The current URI for this page, for reference purposes)
Zhang, Jian: https://orcid.org/0000-0001-8405-2323
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