In the past decade, Sobol’s variance decomposition has been used as a tool to assess how the output of a model is affected by the uncertainty on its input parameters. We show some links between global sensitivity analysis and stochastic ordering theory. More specifically, we study the influence of inputs’ distributions on Sobol indices in relation with stochastic orders. This gives an argument in favor of using Sobol’s indices in uncertainty quantification, as one indicator among others
International audienceThis study compares the performances of two sampling-based strategies for the ...
Sensitivity analysis helps identify which model inputs convey the most uncertainty to the model outp...
International audienceMany mathematical models involve input parameters, which are not precisely kno...
In the past decade, Sobol’s variance decomposition has been used as a tool to assess how the output ...
Abstract. In the past decade, Sobol’s variance decomposition have been used as a tool- among others-...
International audienceIn the past decade, Sobol's variance decomposition have been used as a tool - ...
International audienceWe present a global sensitivity analysis that quantifies the impact of paramet...
Sensitivity analysis assesses the influence of input parameters on the conclusion of a model. Tradit...
Sobol sensitivity indices assess how the output of a given mathematical model is sensitive to its i...
International audienceMany mathematical models involve input parameters, which are not precisely kno...
International audienceThe global sensitivity analysis of a numerical model aims to quantify, by mean...
Approaches for studying uncertainty are of great necessity in all disciplines. While the forward pro...
This study compares the performances of two sampling-based strategies for the simultaneous estimatio...
Sensitivity analysis of model output is relevant to a number of practices, including verification of...
This paper introduces an alternative way of randomizing Sobol′ sequences, called the Column Shift me...
International audienceThis study compares the performances of two sampling-based strategies for the ...
Sensitivity analysis helps identify which model inputs convey the most uncertainty to the model outp...
International audienceMany mathematical models involve input parameters, which are not precisely kno...
In the past decade, Sobol’s variance decomposition has been used as a tool to assess how the output ...
Abstract. In the past decade, Sobol’s variance decomposition have been used as a tool- among others-...
International audienceIn the past decade, Sobol's variance decomposition have been used as a tool - ...
International audienceWe present a global sensitivity analysis that quantifies the impact of paramet...
Sensitivity analysis assesses the influence of input parameters on the conclusion of a model. Tradit...
Sobol sensitivity indices assess how the output of a given mathematical model is sensitive to its i...
International audienceMany mathematical models involve input parameters, which are not precisely kno...
International audienceThe global sensitivity analysis of a numerical model aims to quantify, by mean...
Approaches for studying uncertainty are of great necessity in all disciplines. While the forward pro...
This study compares the performances of two sampling-based strategies for the simultaneous estimatio...
Sensitivity analysis of model output is relevant to a number of practices, including verification of...
This paper introduces an alternative way of randomizing Sobol′ sequences, called the Column Shift me...
International audienceThis study compares the performances of two sampling-based strategies for the ...
Sensitivity analysis helps identify which model inputs convey the most uncertainty to the model outp...
International audienceMany mathematical models involve input parameters, which are not precisely kno...