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Advances in Probabilistic Graphical Models pp 81–102Cite as

Bayesian Network Models with Discrete and Continuous Variables

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 213))

Abstract

Bayesian networks are powerful tools for handling problems which are specified through a multivariate probability distribution. A broad background of theory and methods have been developed for the case in which all the variables are discrete. However, situations in which continuous and discrete variables coexist in the same problem are common in practice. In such cases, usually the continuous variables are discretized and therefore all the existing methods for discrete variables can be applied, but the price to pay is that the obtained model is just an approximation. In this chapter we study two frameworks where continuous and discrete variables can be handled simultaneously without using discretization. These models are based on the CG and MTE distributions.

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Author information

Authors and Affiliations

  1. Department of Economics and Business, Virginia Military Institute, 24450, Lexington, VA, USA

    Barry R. Cobb

  2. Department of Statistics and Applied Mathematics, University of Almería, Ctra. Sacramento s/n, E-04120, Almería, Spain

    Rafael Rumí & Antonio Salmerón

Authors
  1. Barry R. Cobb
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  2. Rafael Rumí
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  3. Antonio Salmerón
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Editor information

Editors and Affiliations

  1. Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 1, 6525 ED, Nijmegen, The Netherlands

    Peter Lucas Dr.

  2. Department of Computer Science and Artificial Intelligence, University of Granada, c/. Daniel Saucedo Aranda, s/n, 18071, Granada, Spain

    José A. Gámez Dr.

  3. Department of Statistics and Applied Mathematics, The University of Almería, La Cañada de San Urbano s/n, 04120, Almería, Spain

    Antonio Salmerón Dr.

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© 2007 Springer-Verlag Berlin Heidelberg

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Cobb, B.R., Rumí, R., Salmerón, A. (2007). Bayesian Network Models with Discrete and Continuous Variables. In: Lucas, P., Gámez, J.A., Salmerón, A. (eds) Advances in Probabilistic Graphical Models. Studies in Fuzziness and Soft Computing, vol 213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68996-6_4

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  • DOI: https://doi.org/10.1007/978-3-540-68996-6_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68994-2

  • Online ISBN: 978-3-540-68996-6

  • eBook Packages: EngineeringEngineering (R0)

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