07-24, 14:10–14:18 (US/Eastern), Tomatis Auditorium
Sensitivity analysis is commonly used to assess how changes in a model's input data affect its results, and to determine the extent to which changes in a set of model input parameters will affect the model's results.
Global sensitivity analysis, which is becoming increasingly widespread, attempts to quantify the uncertainty due to the uncertainty of input factors taken in isolation or in combination with others. Sobol's sensitivity analysis method is widely used as the classic approach to global sensitivity analysis.
Classical Sobol sensitivity indices assume that the distribution of model parameters is fully known for a given model, which is generally difficult to measure in practical problems.
In our context, these parameter distributions may depend on random parameters in several different ways, all associated with real modeling contexts that we will describe. The aim of this study is to determine in which of these contexts the use of these Sobol indices, either classically via a quantity of interest, or via a summary of the quantity of interest, remains relevant.
We created the R package Sobol4RV to deal with sensitivity analyses in random settings and will provide several example of uses in biological settings.
As a full professor at the University of Technology of Troyes, I am mainly interested in applying statistics and machine learning to high-dimensional, process, and network data.