Bayesian Optimal Designs are experimental designs that incorporate prior information and uncertainty into the process of selecting optimal design points for experiments. By using Bayesian principles, these designs aim to maximize the expected utility or information gained from the experiments while considering uncertainties about the parameters of interest. This approach is especially useful in robust optimal designs, where the goal is to ensure that the design remains effective even under model misspecifications or variations in conditions.
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