Bayesian decision-theoretic design of experiments under an alternative model
Decision-theoretic Bayesian design of experiments is considered when the statistical model used to perform the analysis is different to the model a-priori used to design the experiment. Closed form results and large sample approximations are derived for the special case of normal linear models and for general cases, respectively. These are compared to the case when the fitted and designer models are identical.
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