Jeffrey's rule of conditioning generalized to belief functions

Authors:
Philippe Smets
Affiliations:
IRIDIA, Université Libre de Bruxelles, Brussels, Belgium
Venue:
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Year:
1993

Citing 7
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Abstract

Jeffrey's rule of conditioning has been proposed in order to revise a probability measure by another probability function. We generalize it within the framework of the models based on belief functions. We show that several forms of Jeffrey's conditionings can be defined that correspond to the geometrical rule of conditioning and to Dempster's rule of conditioning, respectively.