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- W1999843693 abstract "Abstract To be effective, knowledge-based systems should have a mechanism for representing and reasoning with uncertainty. Bayesian networks are used quite frequently for this purpose, and the final decision suggested by the expert system is a function of the relative strength of various causal relationships embodied in the network and the decision rule used. The relative strengths of the causal relationships, represented as probabilities, are subject to revision when additional information is obtained. The paper shows that the additional information might be of interest only if the decision based on posterior information is different from that obtained from prior information. A model is developed to see how sensitive the decision is to changes in the relative strengths of the causal relationships. Based on this model, a criterion has been developed that has to be satisfied if the additional information is to change the decision. Managers can use this criterion to test whether it will be worth seeking additional information or not." @default.
- W1999843693 created "2016-06-24" @default.
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- W1999843693 date "1992-04-01" @default.
- W1999843693 modified "2023-09-27" @default.
- W1999843693 title "Implications of sensitivity in Bayesian approximate reasoning for expert systems" @default.
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- W1999843693 doi "https://doi.org/10.1016/0950-5849(92)90081-y" @default.
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