Jeffrey's rule of conditioning generalized to belief functions
This work addresses a theoretical extension in uncertainty modeling for researchers in belief functions, but it appears incremental as it adapts an existing rule to a broader framework.
The authors generalized Jeffrey's rule of conditioning from probability measures to belief functions, showing that multiple forms of conditionings can be defined, corresponding to geometrical and Dempster's rules.
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.