Abstract
An iterative procedure is described as a generalization of Bayes' method of updating an a priori assignment over the power set of the frame of discernment using uncertain evidence. In the context of probability kinematics the law of commutativity holds and the convergence is well behaved. the probability assignments of each updating evidence is retained. A general assignment method is also discussed for combining evidences without reference to any prior. the methods described here can be used in the field of Artificial Intelligence for common-sense reasoning and more specifically for treating uncertainty in Expert Systems. They are also relevant for nonmonotonic reasoning, abduction, and learning theory.
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