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Possibilistic Graphical Models

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Computational Intelligence in Data Mining

Part of the book series: International Centre for Mechanical Sciences ((CISM,volume 408))

Abstract

Graphical modeling is an important method to efficiently represent and analyze uncertain information in knowledge-based systems. Its most prominent representatives are Bayesian networks and Markov networks for probabilistic reasoning. which have been well-known for over ten years now. However, they suffer from certain deficiencies, if imprecise information has to be taken into account. Therefore possibilistic graphical modeling has recently emerged as a promising new area of research. Possibilistic networks are a noteworthy alternative to probabilistic networks whenever it is necessary to model both uncertainty and imprecision. Imprecision, understood as set-valued data, has often to be considered in situations in which information is obtained from human observers or imprecise measuring instruments. In this paper we provide an overview on the state of the art of possibilistic networks w.r.t. to propagation and learning algorithms.

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Author information

Authors and Affiliations

  1. Otto-von-Guericke University, Magdeburg, Germany

    C. Borgelt & R. Kruse

  2. TU Braunsweig, Braunsweig, Germany

    J. Gebhardt

Authors
  1. C. Borgelt
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  2. J. Gebhardt
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  3. R. Kruse
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Editor information

Editors and Affiliations

  1. University of Udine, Italy

    Giacomo Della Riccia

  2. Otto-Von-Guericke University, Germany

    Rudolf Kruse

  3. Free University of Berlin, Germany

    Hanz-J. Lenz

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© 2000 Springer-Verlag Wien

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Borgelt, C., Gebhardt, J., Kruse, R. (2000). Possibilistic Graphical Models. In: Della Riccia, G., Kruse, R., Lenz, HJ. (eds) Computational Intelligence in Data Mining. International Centre for Mechanical Sciences, vol 408. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2588-5_3

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  • DOI: https://doi.org/10.1007/978-3-7091-2588-5_3

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83326-1

  • Online ISBN: 978-3-7091-2588-5

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