Fast Computation of Designs Robust to Parameter Uncertainty for Nonlinear Settings

Experimental design in nonlinear settings is complicated by the fact that the efficiency of a design depends on the unknown parameter values. Thus good designs need to be efficient over a range of likely parameter values. Bayesian design criteria provide a natural framework for achieving such robustness, by averaging local design criteria over a prior distribution on the parameters. A major drawback to the use of such criteria is the heavy computational burden that they impose. We present a clever quadrature scheme that greatly improves the feasibility of using Bayesian design criteria. We illustrate the method on some designed experiments.