European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty

ECSQARU 2015: Symbolic and Quantitative Approaches to Reasoning with Uncertainty pp 301-311 | Cite as

Learning Structure in Evidential Networks from Evidential DataBases

  • Narjes Ben HarizEmail author
  • Boutheina Ben Yaghlane
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9161)


Evidential networks have gained a growing interest as a good tool fusing belief function theory and graph theory to analyze complex systems with uncertain data. The graphical structure of these models is not always clear, it can be fixed by experts or constructed from existing data. The main issue of this paper is how to extract the graphical structure of an evidential network from imperfect data stored in evidential databases.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.LARODEC LaboratoryInstitut Supérieur de Gestion de TunisLe BardoTunisia

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