Derivative-free optimal experimental design

We present a new derivative-free method for the calculation of a planned experiment's information content in optimal experimental design. We will prove that our new approach yields the same result as a calculation with the Fisher information matrix if the mathematical model is linear in the model parameters. The new algorithm is derived from a maximum a posteriori update and calculates the statistical moments of the experiment's trajectory employing the unscented transformation as a derivative-free procedure. For a demonstration of the applicability of the algorithm, we will focus on a biological system described by a simple, but very instructive biological growth model.