Optimal experiment design for nonlinear models subject to large prior uncertainties.

Classical experiment design generally yields an experiment that depends on the value of the parameters to be estimated, which are, of course, unknown. Assuming that the model parameters belong to a population with known statistics, we propose to take the a priori parameter uncertainty into account by optimizing the mathematical expectation of a functional of the Fisher information matrix. This optimization is performed with a stochastic approximation algorithm that makes robust experiment design almost as simple as classical D-optimal design. The resulting methodology is applied to the choice of measurement times for multiexponential models.