Sensitivity Analysis - the confusion between definitions

Sensitivity Analysis (SA) is the study of how the variation in the output of a model (numerical or otherwise) can be apportioned, quantitatively or qualitatively to different sources of variation

\cite{AppliedEuropeanCommission2006}.

SA has also been defined mathematically as differentiation of output with respect to input \cite{Saltelli2006}. This confusion is apparent in the reviews of SA techniques for neural networks \cite{Olden2004}, \cite{Gevrey2003}. While Olden \cite{Olden2004} refer to the algorithms using various names, including one called SA; Gevrey \cite{Gevrey2003} uses SA as a generic term encompassing all the techniques used to compare contribution of variables in the neural network model. We will use SA as a generic term encompassing all the various techniques following the definition above given by \cite{AppliedEuropeanCommission2006}.