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Discrete Exponential Bayesian Networks Structure Learning for Density Estimation

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Emerging Intelligent Computing Technology and Applications (ICIC 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 304))

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Abstract

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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Author information

Authors and Affiliations

  1. Laboratory of Probability and Statistics, Faculty of Sciences of Sfax, University of Sfax, Tunisia

    Aida Jarraya & Afif Masmoudi

  2. LINA Computer Science Lab UMR 6241, Knowledge and Decision Team, University of Nantes, France

    Aida Jarraya & Philippe Leray

Authors
  1. Aida Jarraya
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  2. Philippe Leray
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  3. Afif Masmoudi
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, School of Electronics and Information Engineering, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  3. Department of Chemistry, University of Louisville, 2320 South Brook Street, 40292, Louisville, Kentucky, USA

    Xiang Zhang

  4. School of Electrical, Computer & Telecommunications Engineering, The University of Wollongong,, 2522, North Wollongong, NSW, Australia

    Prashan Premaratne

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© 2012 Springer-Verlag Berlin Heidelberg

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Jarraya, A., Leray, P., Masmoudi, A. (2012). Discrete Exponential Bayesian Networks Structure Learning for Density Estimation. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_21

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  • DOI: https://doi.org/10.1007/978-3-642-31837-5_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31836-8

  • Online ISBN: 978-3-642-31837-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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