The University of Illinois at Chicago
The usefulness and popularity of nonlinear models have spurred a large literature on data analysis, but research on design selection has not kept pace. One complication in studying optimal designs for nonlinear models is that information matrices and optimal designs depend on unknown parameters. Besides the popular locally optimal designs strategy, another common approach is to use Bayesian optimal design approach, which typically means an optimality problem has to be solved through numerical approaches. However, very few algorithm approaches are available for Bayesian optimal design.