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Possibilistic Graphical Models

  • C. Borgelt
  • J. Gebhardt
  • R. Kruse
Part of the International Centre for Mechanical Sciences book series (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.

Keywords

Bayesian Network Graphical Model Directed Acyclic Graph Conditional Independence Maximal Clique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 2000

Authors and Affiliations

  • C. Borgelt
    • 1
  • J. Gebhardt
    • 2
  • R. Kruse
    • 1
  1. 1.Otto-von-Guericke UniversityMagdeburgGermany
  2. 2.TU BraunsweigBraunsweigGermany

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