Conditioning in Dempster-Shafer Theory: Prediction vs. Revision

  • Didier Dubois
  • Thierry Denœux
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)


We recall the existence of two methods for conditioning belief functions due to Dempster: one, known as Dempster conditioning, that applies Bayesian conditioning to the plausibility function and one that performs a sensitivity analysis on a conditional probability. We recall that while the first one is dedicated to revising a belief function, the other one is tailored to a prediction problem when the belief function is a statistical model. We question the use of Dempster conditioning for prediction tasks in Smets generalized Bayes theorem approach to the modeling of statistical evidence and propose a modified version of it, that is more informative than the other conditioning rule.


Mass Function Subjective Probability Prediction Problem Belief Function Focal Element 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.IRIT, CNRS and Université de ToulouseToulouseFrance
  2. 2.HEUDIASYC, CNRS and Université de Technologie de CompiègneCompiègneFrance

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