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.
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© 2012 Springer-Verlag Berlin Heidelberg
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Dubois, D., Denœux, T. (2012). Conditioning in Dempster-Shafer Theory: Prediction vs. Revision. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_45
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DOI: https://doi.org/10.1007/978-3-642-29461-7_45
Publisher Name: Springer, Berlin, Heidelberg
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