On the number of support points of maximin and Bayesian optimal designs

@inproceedings{Braess2007OnTN,
  title={On the number of support points of maximin and Bayesian optimal designs},
  author={Dietrich Braess and Holger Dette},
  year={2007}
}
We consider maximin and Bayesian D-optimal designs for nonlinear regression models. The maximin criterion requires the specification of a region for the nonlinear parameters in the model, while the Bayesian optimality criterion assumes that a prior for these parameters is available. On interval parameter spaces, it was observed empirically by many authors that an increase of uncertainty in the prior information (i.e., a larger range for the parameter space in the maximin criterion or a larger… CONTINUE READING

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