Uncertainty in Artificial Intelligence
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Jeffrey's rule of conditioning generalized to belief functions
Philippe Smets
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.
Pages: 500-505
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PDF Link: /papers/93/p500-smets.pdf
AUTHOR = "Philippe Smets ",
TITLE = "Jeffrey's rule of conditioning generalized to belief functions",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "500--505"

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