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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) (KAR id:31585)

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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


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: (The current URI for this page, for reference purposes)
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