Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models - Hardcover

Über die Autorin bzw. den Autor

Andrea Saltelli, Joint Research Centre of the European Commission, Ispra, Unit of Applied Statistics and Econometrics. Presently leading the Econometric and Applied Statistics Unit of the Joint Research Centre, lead author Professor Saltelli has published many articles in numerous journals over the last 30 years. He is also the main author and main editor of two previous books (both for Wiley).

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Sensitivity analysis is the study of how variation in the output of a statistical model can be apportioned, qualitatively or quantitatively, to different sources of variation. It should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. Choosing the most appropriate method of sensitivity analysis for a particular model can be complex, and depends on a number of factors.

Sensitivity Analysis in Practice guides applied scientists through their modelling problem enabling them to choose and apply the most appropriate sensitivity analysis method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It includes discussion of implementation of the methods using SIMLAB – freely available sensitivity analysis software.

• Provides an accessible overview of the most widely used sensitivity analysis methods.
• Covers a range of methodologies, including variance-based methods, Bayesian uncertainty estimation, selected screening methods and Monte Carlo filtering.
• Opens with a detailed worked example to explain the motivation behind methods described.
• Includes a range of further worked examples to illustrate the practical applications.
• Discusses implementation of the methods using SIMLAB – freely available sensitivity analysis software.
• Contains a large number of references to sources for further reading.
• Supported by a Website featuring a SIMLAB download, data sets and additional material.

Sensitivity Analysis in Practice is primarily aimed at researchers and practitioners working in scientific modelling from statistics, mathematics, medicine, and environmental science. The book is suitable for applied scientists working with statistical models in virtually any discipline, including economics, biology, chemistry and engineering, as well as graduate students of statistical modelling.

Aus dem Klappentext

Sensitivity analysis is the study of how variation in the output of a statistical model can be apportioned, qualitatively or quantitatively, to different sources of variation. It should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. Choosing the most appropriate method of sensitivity analysis for a particular model can be complex, and depends on a number of factors.

Sensitivity Analysis in Practice guides applied scientists through their modelling problem enabling them to choose and apply the most appropriate sensitivity analysis method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It includes discussion of implementation of the methods using SIMLAB – freely available sensitivity analysis software.

• Provides an accessible overview of the most widely used sensitivity analysis methods.
• Covers a range of methodologies, including variance-based methods, Bayesian uncertainty estimation, selected screening methods and Monte Carlo filtering.
• Opens with a detailed worked example to explain the motivation behind methods described.
• Includes a range of further worked examples to illustrate the practical applications.
• Discusses implementation of the methods using SIMLAB – freely available sensitivity analysis software.
• Contains a large number of references to sources for further reading.
• Supported by a Website featuring a SIMLAB download, data sets and additional material.

Sensitivity Analysis in Practice is primarily aimed at researchers and practitioners working in scientific modelling from statistics, mathematics, medicine, and environmental science. The book is suitable for applied scientists working with statistical models in virtually any discipline, including economics, biology, chemistry and engineering, as well as graduate students of statistical modelling.