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Fast Computation of Designs Robust to Parameter Uncertainty for Nonlinear Settings

Summary: [This abstract is based on the authors' abstract.] Good experimental designs need to be efficient over a range of likely parameter values. Bayesian design criteria provide such robustness by averaging local design criteria over a prior distribution on the parameters, but they impose a heavy computational burden. A quadrature scheme is proposed that improves the feasibility of using Bayesian design criteria. The method is illustrated on some designed experiments.

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  • Topics: Design of Experiments, Product-Service Design
  • Keywords: Design of experiments (DOE), Bayesian methods, D-optimality, Linear models, Nonlinear models
  • Author: Gotwalt, Christopher M.; Jones, Bradley A.; Steinberg, David M.
  • Journal: Technometrics