Discover the essential role of mathematical modeling in modern society and technology with this comprehensive guide to global sensitivity analysis. While models are invaluable for translating assumptions into meaningful inferences, their reliability often comes into question, especially when applied to critical and controversial themes such as health, the environment, or the economy. This book addresses the increasing demand for rigorous standards of proof, particularly when model outputs relate to potential financial losses, threats to human health, or environmental conditions.
Mathematical models rely heavily on assumptions about system laws, status, and numerous internal variables. However, many of these assumptions are fraught with uncertainties, raising important questions about the dependability of model-based inferences. To tackle these challenges, the book introduces Global Sensitivity Analysis as an accessible approach rooted in quantitative sensitivity analysis. It guides readers through the fundamental principles, recommended practices, and a rich set of solved exercises, making complex concepts approachable and practical.
This self-contained volume provides a thorough treatment of the subject, enabling readers to learn and apply global sensitivity analysis independently. It covers how to frame analyses, interpret results, and avoid common pitfalls. The inclusion of numerous exercises and solved problems helps illustrate real-world applications, making the material engaging and practical. Authored by leading practitioners in the field, the book benefits from a diverse range of disciplinary perspectives.
Designed for postgraduate students and professionals across various fields—including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics—this book offers valuable insights for anyone involved in risk analysis, financial modeling, or environmental assessment. It is equally useful for engineers working on risk management and financial analysts focused on pricing and hedging, providing essential tools to enhance the reliability and robustness of model-based inferences.