Sample-based evaluation of global probabilistic sensitivity measures

摘要

The evaluation of probabilistic sensitivity requires scalar measures quantifying the difference between probability density functions. The efficient estimation of two such alternative measures (relative entropy and Hellinger distance) is investigated within the setting of a global sensitivity analysis. This analysis requires evaluation of these measures with respect to different marginal densities, all connected to the same auxiliary joint density. Estimation of these densities is based on implementation of kernel density estimation, using information from samples from their joint distribution. These samples are further explored for improving the computational efficiency whereas theoretical concepts related to these two measures are addressed.