AJP - Regulatory, Integrative and Comparative Physiology, Vol 253, Issue 3 530-R534, Copyright © 1987 by American Physiological Society
Optimal experiment design for nonlinear models subject to large prior uncertainties
E. Walter and L. Pronzato
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.
