Sensitivity analysis assesses the influence of input parameters on the conclusion of a model. Traditional analysis methods—based on evaluating the model at a reference parameter vector and changing one parameter at a time—are local, linear, and usually do not capture interactions among the...

Sensitivity analysis assesses the influence of input parameters on the conclusion of a model. Traditional analysis methods—based on evaluating the model at a reference parameter vector and changing one parameter at a time—are local, linear, and usually do not capture interactions among the...

We present a global sensitivity analysis that quantifies the impact of parameter uncertainty on model outcomes. Specifically, we propose variance-decomposition-based Sobol' indices to establish an importance ranking of parameters and univariate effects to determine the direction of their impact....

We present a global sensitivity analysis that quantifies the impact of parameter uncertainty on model outcomes. Specifically, we propose variance‐decomposition‐based Sobol' indices to establish an importance ranking of parameters and univariate effects to determine the direction of their...