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The Bayesian D criterion modifies the D‐optimality approach to reduce dependence of the selected design on an assumed model. This criterion has been applied to select various single‐stratum designs for completely randomized experiments when the number of effects is greater than the sample size. In many industrial experiments, complete randomization is sometimes expensive or infeasible, and hence,...
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