DISTRIBUTED REVISION OF COMPOSITE BELIEFS
Publisher Summary
This chapter highlights Bayesian analysis and belief networks for the revision of belief commitment, which is the categorical but tentative acceptance of a subset of hypotheses that together constitute the most satisfactory explanation of the evidence at hand. In probabilistic terms, belief commitment amounts to finding the most probable instantiation of all hypothesis variables, given the observed data. The resulting output is an optimal list of jointly accepted propositions. It is a list that may change abruptly as more evidence is obtained. The chapter presents a comparison of belief updating and belief revision highlighting a simple example from circuit diagnosis. ...
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