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New Propagation Algorithm in Dynamic Directed Evidential Networks with Conditional Belief Functions

  • Wafa Laâmari
  • Boutheina Ben Yaghlane
  • Christophe Simon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8032)

Abstract

Proposed as a subclass of directed evidential network with conditional belief functions (DEVN), dynamic directed evidential network with conditional belief functions (DDEVN) was introduced as a new approach for modeling systems evolving in time. Considered as an alternative to dynamic Bayesian network and dynamic possibilistic network, this framework enables to reason under uncertainty expressed in the belief function formalism. In this paper, we propose a new propagation algorithm in DDEVNs based on a new computational structure, namely the mixed binary join tree, which is appropriate for making the exact inference in these networks.

Keywords

Time Slice Fusion Algorithm Inference Algorithm Belief Function Dynamic Bayesian Network 
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 2013

Authors and Affiliations

  • Wafa Laâmari
    • 1
  • Boutheina Ben Yaghlane
    • 1
  • Christophe Simon
    • 2
  1. 1.LARODEC LaboratoryInstitut Supérieur de Gestion de TunisTunisia
  2. 2.CNRS, UMR 7039CRAN - Université de LorraineFrance

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