In many fields, such as environmental risk assessment, agronomic system behavior,
aerospace engineering, and nuclear safety, mathematical models turned into computer code …

The aim of these Notes is to increase the reader's understanding of the spread of infectious
diseases using mathematics, and in particular stochastic methods. Needless to say …

We describe a novel attribution method which is grounded in Sensitivity Analysis and uses
Sobol indices. Beyond modeling the individual contributions of image regions, Sobol indices …

Many mathematical models involve input parameters, which are not precisely known. Global
sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact …

Global sensitivity analysis with variance-based measures suffers from several theoretical
and practical limitations, since they focus only on the variance of the output and handle …

We propose a new statistical estimation framework for a large family of global sensitivity
analysis indices. Our approach is based on rank statistics and uses an empirical correlation …

The Shapley effects are global sensitivity indices: they quantify the impact of each input
variable on the output variable in a model. In this work, we suggest new estimators of these …

Global sensitivity analysis is the main quantitative technique for identifying the most
influential input variables in a numerical simulation model. In particular when the inputs are …

Reliability-oriented sensitivity analysis aims at combining both reliability and sensitivity
analyses by quantifying the influence of each input variable of a numerical model on a …

Global sensitivity analysis provides insight into how sources of uncertainty contribute to
uncertainty in predictions of computational models. Global sensitivity indices, also called …