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 Saltelli
et al, 2008, Global
sensitivity analysis: the primer
Complex mathematical and computational models are used in all areas of
society and technology and yet model based science is increasingly contested
or refuted, especially when models are applied to controversial themes
in domains such as health, the environment or the economy. More stringent
standards of proofs are demanded from model-based numbers, especially
when these numbers represent potential financial losses, threats to human
health or the state of the environment. Quantitative sensitivity analysis
is generally agreed to be one such standard. Mathematical models are good
at mapping assumptions into inferences. A modeller makes assumptions about
laws pertaining to the system, about its status and a plethora of other,
often arcane, system variables and internal model settings. To what extent
can we rely on the model-based inference when most of these assumptions
are fraught with uncertainties? Global Sensitivity Analysis offers an
accessible treatment of such problems via quantitative sensitivity analysis,
beginning with the first principles and guiding the reader through the
full range of recommended practices with a rich set of solved exercises.
The text explains the motivation for sensitivity analysis, reviews the
required statistical concepts, and provides a guide to potential applications.
The book: Provides a self-contained treatment of the subject, allowing
readers to learn and practice global sensitivity analysis without further
materials. Presents ways to frame the analysis, interpret its results,
and avoid potential pitfalls. Features numerous exercises and solved problems
to help illustrate the applications. Is authored by leading sensitivity
analysis practitioners, combining a range of disciplinary backgrounds.
Postgraduate students and practitioners in a wide range of subjects, including
statistics, mathematics, engineering, physics, chemistry, environmental
sciences, biology, toxicology, actuarial sciences, and econometrics will
find much of use here. This book will prove equally valuable to engineers
working on risk analysis and to financial analysts concerned with pricing
and hedging.
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 Saltelli
et al., 2000, Sensitivity
Analysis
Sensitivity analysis is used to ascertain how a given model output depends
upon the input parameters. This is an important method for checking the
quality of a given model, as well as a powerful tool for checking the
robustness and reliability of its analysis. The topic is acknowledged
as essential for good modelling practice, and is an implicit part of any
modelling field.
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