Conditioning in Dempster-Shafer Theory: Prediction vs. Revision

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Abstract

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.

Keywords

Mass Function Subjective Probability Prediction Problem 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 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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