Learning Bayesian Networks Structure with Continuous Variables

  • Shuang-Cheng Wang
  • Xiao-Lin Li
  • Hai-Yan Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


In this paper, a new method for learning Bayesian networks structure with continuous variables is proposed. The continuous variables are discretized based on hybrid data clustering. The discrete values of a continuous variable are obtained by using father node structure and Gibbs sampling. Optimal dimension of discretized continuous variable is found by MDL principle to the Markov blanket. Dependent relationship is refined by optimization regulation to Bayesian network structure in iteration learning.


Bayesian Network Gibbs Sampling Maximal Likelihood Tree Chain Graph Bayesian Network Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shuang-Cheng Wang
    • 1
    • 2
  • Xiao-Lin Li
    • 3
  • Hai-Yan Tang
    • 2
  1. 1.Department of Information ScienceShanghai Lixin University of CommerceShanghaiChina
  2. 2.China Lixin Risk Management Research InstituteShanghai Lixin University of CommerceShanghaiChina
  3. 3.National Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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