This book provides an overview of global sensitivity analysis methods and algorithms, including their theoretical basis and mathematical properties. The authors use real case studies and numerous examples, illustrated throughout using R code, to explain their use and usefulness in practice. The book covers topics including Sobol' indices, sampling-based formulas, spectral methods, metamodel-based approaches for estimation purposes, screening techniques devoted to identifying influential and noninfluential inputs, variance-based measures for model inputs that are statistically dependent, and a case study in R related to a COVID-19 epidemic model. This book is intended for engineers, researchers, and undergraduate students with a solid background in statistics and probability theory.
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