Zhang, Jian (2009) Learning Bayesian networks for discrete data. Computational Statistics and Data Analysis, 53 (4). pp. 865-876. ISSN 0167-9473 .
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| Official URL http://dx.doi.org/10.1016/j.csda.2008.10.007 |
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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.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics |
| Divisions: | Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics |
| Depositing User: | Jian Zhang |
| Date Deposited: | 11 Oct 2012 17:21 |
| Last Modified: | 20 Feb 2013 16:57 |
| Resource URI: | http://kar.kent.ac.uk/id/eprint/31585 (The current URI for this page, for reference purposes) |
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