Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Handbook of logic in artificial intelligence and logic programming (Vol. 4)
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We develop a probabilistic criterion for belief expansion that is sensitive to the degree of contextual fit of the new information to our belief set as well as to the reliability of our information source. We contrast our approach with the success postulate in AGM-style belief revision and show how the idealizations in our approach can be relaxed by invoking Bayesian-Network models.