Representing Belief Function Knowledge with Graphical Models

  • Imen Boukhris
  • Salem Benferhat
  • Zied Elouedi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7091)


Belief function theory is an appropriate framework to model different forms of knowledge including probabilistic knowledge. One simple and efficient way to reason under uncertainty, is the use of compact graphical models, namely directed acyclic graphs. Therefore naturally, a question crosses the mind: If we deal with Bayesian belief knowledge does the network collapse into a Bayesian network? This paper attempts to answer this question by analyzing different forms of belief function networks defined with conditional beliefs defined either with a unique conditional distribution for all parents or a single conditional distribution for each single parent. We also propose a new method for the deconditionalization process to compute the joint distribution.


Bayesian Network Directed Acyclic Graph Child Node Belief Function Focal Element 
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 2011

Authors and Affiliations

  • Imen Boukhris
    • 1
    • 2
  • Salem Benferhat
    • 2
  • Zied Elouedi
    • 1
  1. 1.LARODECUniversité de Tunis, ISG de TunisTunisia
  2. 2.Faculté Jean PerrinCRIL, Université d’ArtoisFrance

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