Sample-based evaluation of global probabilistic sensitivity measures

• Focus is on computationally efficient approaches for global probabilistic sensitivity analysis.
• Sensitivity analysis is established by comparing two distributions of the model parameters.
• Relative entropy and Hellinger distance are proposed for quantification of sensitivity.
• Sample-based approach is considered for efficient approximation using kernel density estimation.
• Theoretical issues related to the sensitivity analysis are also discussed.

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.