This book provides an overview of global sensitivity analysis methods and algorithms, including their theoretical basis and mathematical properties. The authors use a practical point of view and real case studies as well as numerous examples, and applications of the different approaches are illustrated throughout using R code to explain their usage and usefulness in practice. Basics and Trends in Sensitivity Analysis: Theory and Practice in R covers a lot of material, including theoretical aspects of Sobol’ indices as well as sampling-based formulas, spectral methods, and metamodel-based approaches for estimation purposes; screening techniques devoted to identifying influential and noninfluential inputs; variance-based measures when model inputs are statistically dependent (and several other approaches that go beyond variance-based sensitivity measures); and a case study in R related to a COVID-19 epidemic model where the full workflow of sensitivity analysis combining several techniques is presented. This book is intended for engineers, researchers, and undergraduate students who use complex numerical models and have an interest in sensitivity analysis techniques and is appropriate for anyone with a solid mathematical background in basic statistical and probability theories who develops and uses numerical models in all scientific and engineering domains.

About the Author

S�bastien Da Veiga is a senior expert in statistics and optimization at Safran, an international high-technology group and tier-1 supplier of systems and equipment in the aerospace and defense markets. His research interests include computer experiments modeling, sensitivity analysis, optimization problems, kernel methods, and random forests. Fabrice Gamboa is a professor at Toulouse Mathematics Institute. His research interests range from applied probability to industrial statistics. Bertrand Iooss is a senior researcher in industrial statistics and safety at �lectricit� de France, an international leading energy producer and supplier. His research interests involve the design, analysis, modeling, and uncertainty management in computer experiments, as well as the topics of interpretability and validation of machine learning techniques. Cl�mentine Prieur is a professor at University Grenoble Alpes. Her research focuses on uncertainty quantification, sensitivity analysis, model order reduction, nonparametric inference, and risk analysis with mainly environmental applications.

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