Summary
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.
A modification of Owen's method is presented for the computer calculation of the bi-variate normal integral. The modification consists of an improved method for evaluating Owen's T function which considerably reduces the computation time required. Using the modification, Owen's method is a simple, fast and accurate technique for computing the bi-variate integral for all values of the correlation coefficient and of the limits of integration.
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