title: Optimal experiment design for nonlinear models subject to large prior uncertainties.
abstract: 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.
mesh_terms: D008962:Models, Theoretical; D012107:Research Design; D013269:Stochastic Processes