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Method for Refining a Priori Probabilities of a Complex System States from Experimental Data

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For a complex system, the problem of refining a priori probabilities of hypotheses about the system state using the data obtained during its experimental adjustment and operation is considered. The proposed method for solving this problem is based on weighted accounting of the results of a priori and experimental studies of the system. The results of comparing a posteriori estimates with estimates obtained by Bayes formulas are presented. It is shown that, in contrast to the Bayesian solution, the use of the proposed method provides for accounting the proximity of the results of a priori studies of the system to the results of experiments, and does not allow a priori information to dominate the experimental data.

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