Basics and trends in sensitivity analysis : theory and practice in R / Sébastien Da Veiga, Fabrice Gamboa, Bertrand Iooss, Clémentine Prieur.

Format:

Book

Author/Creator:

Veiga, Sébastien da, author.

Gamboa, Fabrice, author.

Iooss, Bertrand, author.

Prieur, Clémentine, author.

Contributor:

Society for Industrial and Applied Mathematics, publisher.

Series:

Computational science and engineering.

Computational science & engineering

Language:

English

Subjects (All):

Sensitivity theory (Mathematics).

Global analysis (Mathematics).

R (Computer program language).

Physical Description:

1 online resource (xvi, 291 pages) : illustrations

Place of Publication:

Philadelphia, Pennsylvania : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104) : Mathematical Optimization Society, [2021]

System Details:

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Summary:

his 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); 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.

Contents:

A first look at screening using R

Variance-based sensitivity measures

Spectral and metamodel-based estimation

Variance-based sensitivity measures with dependent inputs

Beyond variance-based indices

A case study in R : COVID-19 epidemic model.

Notes:

Includes bibliographical references (pages 259-288) and index.

Description based on title page of print version.

ISBN:

1-61197-669-3

Publisher Number:

CS23 SIAM