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A Total Entropy Criterion for the Dual Problem of Model Discrimination and Parameter Estimation

David M. Borth
Journal of the Royal Statistical Society. Series B (Methodological)
Vol. 37, No. 1 (1975), pp. 77-87
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/2984993
Page Count: 11
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A Total Entropy Criterion for the Dual Problem of Model Discrimination and Parameter Estimation
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Abstract

A total entropy criterion is developed for the sequential design of experiments. The criterion is applicable to the dual problem of model discrimination and parameter estimation. The total entropy measures both the uncertainty about which mathematical model is correct and the uncertainty about the parameter vector for each model. The criterion is shown to lead to the choice of experiment for which the outcome is most uncertain, relative to the uncertainty due to experimental error.

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