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Learning Bayesian Network Structures When Discrete and Continuous Variables Are Present

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Probabilistic Graphical Models (PGM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8754))

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Abstract

In any database, some fields are discrete and others continuous in each record. We consider learning Bayesian network structures when discrete and continuous variables are present. Thus far, most of the previous results assumed that all the variables are either discrete or continuous. We propose to compute a new Bayesian score for each subset of discrete and continuous variables, and to obtain a structure that maximizes the posterior probability given examples. We evaluate the proposed algorithm and make experiments to see that the error probability and Kullback-Leibler divergence diminish as n grows whereas the computation increases linearly in the logarithm of the number of bins in the histograms that approximate the density.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Osaka University, Toyonaka, Osaka, 560-0043, Japan

    Joe Suzuki

Authors
  1. Joe Suzuki
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Editor information

Editors and Affiliations

  1. Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC Utrecht, The Netherlands

    Linda C. van der Gaag

  2. Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC Utrecht,, The Netherlands

    Ad J. Feelders

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Suzuki, J. (2014). Learning Bayesian Network Structures When Discrete and Continuous Variables Are Present. In: van der Gaag, L.C., Feelders, A.J. (eds) Probabilistic Graphical Models. PGM 2014. Lecture Notes in Computer Science(), vol 8754. Springer, Cham. https://doi.org/10.1007/978-3-319-11433-0_31

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  • DOI: https://doi.org/10.1007/978-3-319-11433-0_31

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11432-3

  • Online ISBN: 978-3-319-11433-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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