When a decision maker receives data bearing on an uncertain event, the a priori probability of the event can be updated by computing the conditional probability of the uncertain hypothesis given the new evidence. The derivation of the revised or a posteriori probability can be easily derived from fundamental principles and its discovery has been attributed to the Reverend Thomas Bayes (1763). The result is therefore known as Bayes rule or theorem:
In this equation, H 1 refers to the specific, uncertain hypothesis entertained by the decision maker, the {H i} are the complete set of possible hypotheses, and E refers to the new evidence or information received.
Bayesian decision theory, Subjective probability, and Utility.
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© 2001 Kluwer Academic Publishers
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Gass, S.I., Harris, C.M. (2001). BAYES RULE . In: Gass, S.I., Harris, C.M. (eds) Encyclopedia of Operations Research and Management Science. Springer, New York, NY. https://doi.org/10.1007/1-4020-0611-X_67
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DOI: https://doi.org/10.1007/1-4020-0611-X_67
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