• Corpus ID: 249953720

Variance-based global sensitivity analysis of numerical models using R

  title={Variance-based global sensitivity analysis of numerical models using R},
  author={Hossein Mohammadi and Peter Challenor and Cl{\'e}mentine Prieur},
Sensitivity analysis plays an important role in the development of computer models/simulators through identifying the contribution of each (uncertain) input factor to the model output variability. This report investigates different aspects of the variance-based global sensitivity analysis in the context of complex black-box computer codes. The analysis is mainly conducted using two R packages, namely sensobol (Puy et al., 2021) and sensitivity (Iooss et al., 2021). While the package sensitivity… 

Figures from this paper



Global sensitivity analysis of stochastic computer models with joint metamodels

Results show that the joint modeling approach yields accurate sensitivity index estimators even when heteroscedasticity is strong, which is good news for stochastic computer codes, for which the result of each code run is itself random.

A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output

Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

Uncertainty Quantification and Global Sensitivity Analysis for Economic Models

This work proposes Sobol’ indices, which are based on variance decomposition, and exemplifies their use with a standard real business cycle model and uses this polynomial representation to evaluate the univariate effects, which can be interpreted as a robust impact of a parameter on the model conclusions.

Global sensitivity analysis using polynomial chaos expansions

  • B. Sudret
  • Mathematics
    Reliab. Eng. Syst. Saf.
  • 2008

Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models

This paper proposes to use the generalized lambda model to emulate the response distribution of stochastic simulators and confirms the convergence of the approach for estimating the sensitivity indices even with the presence of strong heteroscedasticity and small signal-to-noise ratio.

Sensitivity analysis: A review of recent advances

Sensitivity analysis of environmental models: A systematic review with practical workflow

sensobol: An R Package to Compute Variance-Based Sensitivity Indices

The R package sensobol provides several functions to conduct variance-based uncertainty and sensitivity analysis, from the estimation of sensitivity indices to the visual representation of the