Description
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
Sebastien Da Veiga is a senior expert in Statistics and Optimization at Safran. His research interests include computer experiments modelling, sensitivity analysis, optimization problems, kernel methods, and random forests.
Fabrice Gamboa is currently a professor at Toulouse University. His research interests include asymptotic statistics, random matrices and large deviations, statistical modelling, and industrial applications.
Bertrand Iooss is a senior researcher at EDF R&D, leading a project on uncertainty quantification and machine learning techniques for nuclear engineering processes. His research interests include computer experiments modelling, sensitivity analysis, geostatistics, machine learning validation, and explainability.
Clementine Prieur is a professor at University Grenoble Alpes. Her research interests include properties of dependent stochastic processes and modelling of spatio-temporal dependence.
Book Information
ISBN 9781611976687
Author Sebastien Da Veiga
Format Paperback
Page Count 293
Imprint Society for Industrial & Applied Mathematics,U.S.
Publisher Society for Industrial & Applied Mathematics,U.S.
Weight(grams) 650g
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
Sebastien Da Veiga is a senior expert in Statistics and Optimization at Safran. His research interests include computer experiments modelling, sensitivity analysis, optimization problems, kernel methods, and random forests.
Fabrice Gamboa is currently a professor at Toulouse University. His research interests include asymptotic statistics, random matrices and large deviations, statistical modelling, and industrial applications.
Bertrand Iooss is a senior researcher at EDF R&D, leading a project on uncertainty quantification and machine learning techniques for nuclear engineering processes. His research interests include computer experiments modelling, sensitivity analysis, geostatistics, machine learning validation, and explainability.
Clementine Prieur is a professor at University Grenoble Alpes. Her research interests include properties of dependent stochastic processes and modelling of spatio-temporal dependence.
Book Information
ISBN 9781611976687
Author Sebastien Da Veiga
Format Paperback
Page Count 293
Imprint Society for Industrial & Applied Mathematics,U.S.
Publisher Society for Industrial & Applied Mathematics,U.S.
Weight(grams) 650g
