A new derivative free method for optimal experimental design utilizing a maximum a posteriori update
M. Kawohl, Thomas Heine · 2007
We present a new derivative free method for the calculation of the information content of a planned experiment in optimal experimental design (OED). It is shown that in case of a mathematical model that is linear in the model parameters the new approach yields the same result as a calculation with the Fisher Information Matrix (FIM). The new algorithm is derived from a maximum a posteriori (MAP) update. The needed statistical moments of the planned trajectory are calculated using a derivative free approach, the Unscented Transformation (UT). The algorithm is demonstrated using a simple, but very instructive biological growth model.