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Background to and Perspectives on Possibilistic Graphical Models

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Applications of Uncertainty Formalisms

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1455))

Abstract

Graphical modelling is an important tool for the efficient representation and analysis of uncertain information in knowledge-based systems. While Bayesian networks and Markov networks from probabilistic graphical modelling have been well-known for about ten years, the field of possibilistic graphical modelling appears to be a new promising area of research. Possibilistic networks provide an alternative approach compared to probabilistic networks, whenever it is necessary to model uncertainty and imprecision as two different kinds of imperfect information. Imprecision in the sense of multivalued data has often to be considered in situations where information is obtained from human observations or non-precise measurement units. In this contribution we present a comparison of the background and perspectives of probabilistic and possibilistic graphical models, and give an overview on the current state of the art of possibilistic networks with respect to propagation and learning algorithms, applicable to data mining and data fusion problems.

This work is an update of contributions made to ECAI’96 and ECSQARU/FAPR’97

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Author information

Authors and Affiliations

  1. Dept. of Mathematics and Computer Science, University of Braunschweig, 38106, Braunschweig, Germany

    Jörg Gebhardt

  2. Dept. of Computer Science, Otto-von-Guericke University, 39106, Magdeburg, Germany

    Rudolf Kruse

Authors
  1. Jörg Gebhardt
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  2. Rudolf Kruse
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    Anthony Hunter

  2. Queen Mary and Westfield College, Department of Electronic Engineering, University of London, London, E1 4NS, UK

    Simon Parsons

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© 1998 Springer-Verlag Berlin Heidelberg

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Gebhardt, J., Kruse, R. (1998). Background to and Perspectives on Possibilistic Graphical Models. In: Hunter, A., Parsons, S. (eds) Applications of Uncertainty Formalisms. Lecture Notes in Computer Science(), vol 1455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49426-X_18

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  • DOI: https://doi.org/10.1007/3-540-49426-X_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65312-7

  • Online ISBN: 978-3-540-49426-3

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