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Probability-Possibility Transformation:

Application to Bayesian and Possibilistic Networks
  • Yosra Ben Slimen
  • Raouia Ayachi
  • Nahla Ben Amor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8256)

Abstract

Probability-possibility transformation is a purely mechanical transformation of probabilistic support to possibilistic support and vice versa. In this paper, we apply the most common transformations to graphical models, i.e., Bayesian into possibilistic networks. We show that existing transformations are not appropriate to transform Bayesian networks to possibilistic ones since they cannot preserve the information incorporated in joint distributions. Therefore, we propose new consitency properties, exclusively useful for graphical models transformations.

Keywords

Probability-Possibility transformation Bayesian networks Possibilistic networks 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Yosra Ben Slimen
    • 1
  • Raouia Ayachi
    • 1
  • Nahla Ben Amor
    • 1
  1. 1.Institut Supérieur de Gestion TunisLARODECLe BardoTunisie

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