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A Gaussian mixed model for learning discrete Bayesian networks

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  • Balov, Nikolay

Abstract

In this paper, we address the problem of learning discrete Bayesian networks from noisy data. A graphical model based on a mixture of Gaussian distributions with categorical mixing structure coming from a discrete Bayesian network is considered. The network learning is formulated as a maximum likelihood estimation problem and performed by employing an EM algorithm. The proposed approach is relevant to a variety of statistical problems for which Bayesian network models are suitable--from simple regression analysis to learning gene/protein regulatory networks from microarray data.

Suggested Citation

  • Balov, Nikolay, 2011. "A Gaussian mixed model for learning discrete Bayesian networks," Statistics & Probability Letters, Elsevier, vol. 81(2), pages 220-230, February.
  • Handle: RePEc:eee:stapro:v:81:y:2011:i:2:p:220-230
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    References listed on IDEAS

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    1. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
    2. Salzman Peter & Almudevar Anthony, 2006. "Using Complexity for the Estimation of Bayesian Networks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-23, August.
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