Discrete Exponential Bayesian Networks Structure Learning for Density Estimation

  • Aida Jarraya
  • Philippe Leray
  • Afif Masmoudi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)


Our work aims at developing or expliciting bridges between Bayesian Networks and Natural Exponential Families, by proposing discrete exponential Bayesian networks as a generalization of usual discrete ones. In this paper, we illustrate the use of discrete exponential Bayesian networks for Bayesian structure learning and density estimation. Our goal is to empirically determine in which contexts these models can be a good alternative to usual Bayesian networks for density estimation.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Aida Jarraya
    • 1
    • 2
  • Philippe Leray
    • 2
  • Afif Masmoudi
    • 1
  1. 1.Laboratory of Probability and Statistics, Faculty of Sciences of SfaxUniversity of SfaxTunisia
  2. 2.LINA Computer Science Lab UMR 6241, Knowledge and Decision TeamUniversity of NantesFrance

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