Jeffrey's rule of conditioning generalized to belief functions
arXiv:1303.1514
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.
Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)