  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.
