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Learning Bayesian networks for discrete data

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  • Liang, Faming
  • Zhang, Jian

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.

Suggested Citation

  • Liang, Faming & Zhang, Jian, 2009. "Learning Bayesian networks for discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 865-876, February.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:865-876
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    References listed on IDEAS

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    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. Ellis, Byron & Wong, Wing Hung, 2008. "Learning Causal Bayesian Network Structures From Experimental Data," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 778-789, June.
    3. Liang, Faming & Liu, Chuanhai & Carroll, Raymond J., 2007. "Stochastic Approximation in Monte Carlo Computation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 305-320, March.
    4. Liang F., 2002. "Dynamically Weighted Importance Sampling in Monte Carlo Computation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 807-821, September.
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    Cited by:

    1. P. Fuster-Parra & A. García-Mas & F. Ponseti & P. Palou & J. Cruz, 2014. "A Bayesian network to discover relationships between negative features in sport: a case study of teen players," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(3), pages 1473-1491, May.
    2. Yoo, Changwon, 2012. "The Bayesian method for causal discovery of latent-variable models from a mixture of experimental and observational data," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2183-2205.

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