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Background and perspectives of possibilistic graphical models

  • Jörg Gebhardt
  • Rudolf Kruse
Invited Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1244)

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 are well-known for a couple of years, the field of possibilistic graphical modelling occurs as 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 set-valued data has often to be considered in situations where data are 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.

Keywords

Graphical Model Directed Acyclic Graph Conditional Independence Possibility Distribution Possibility Theory 
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 Berlin Heidelberg 1997

Authors and Affiliations

  • Jörg Gebhardt
    • 1
  • Rudolf Kruse
    • 2
  1. 1.Dept. of Mathematics and Computer ScienceUniversity of BraunschweigBraunschweigGermany
  2. 2.Dept. of Computer ScienceOtto-von-Guericke UniversityMagdeburgGermany

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