This paper examines how a number of sensitivity techniques may be adapted to deal with the analysis of real experiments. We show that the fundamental quantities in this context are the partial derivatives of model input parameters with respect to experimental observables. They appear as a consequence of the procedure used to fit the model to the experiment. These so-called experimental elementary sensitivities are then combined with the usual model elementary sensitivities to yield coefficients which relate experimental and model observables. The importance of such coefficients for the planning of experiments is, in particular, discussed. Based on a linear analysis, we also derive simple expressions (containing the experimental elementary sensitivities) for the degree of parameter deviation arising from uncertainties in and discrepancies between model and measured observables. Finally, an overview of the role of sensitivity theory in analyzing experimental data is given.
All Science Journal Classification (ASJC) codes
- Computational Mathematics
- Applied Mathematics