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Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB - a widely distributed freely-available sensitivity...

The Wiley Paperback Series makes valuable content more accessible to a new generation of statisticians, mathematicians and scientists.

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.

• Offers an accessible introduction to sensitivity analysis.
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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.

- Offers an accessible introduction to sensitivity analysis

- Covers all the latest research

- Illustrates concepts with numerous examples, applications and case studies

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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...

----- One of the arguments most frequently raised by objectors to nuclear power production is the spectrum of the hazard from the nuc.lear waste. In the public's perception one of the most dangerous features of this waste is the threat to future generations due to the long life of the radio-emitting nuclides. People's concern for this hazard seems to be greater than that for other types of toxic wastes, widely produced and released to the environment (heavy metals, PCBs, arsenic etc.) though these might last even longer. In fact public attitude toward risk depends very strongly on the nature...

A crisis looms over the scientific enterprise. Not a day passes without news of retractions, failed replications, fraudulent peer reviews, or misinformed science-based policies. The social implications are enormous, yet this crisis has remained largely uncharted-until now.

In Science on the Verge, luminaries in the field of post-normal science and scientific governance focus attention on worrying fault-lines in the use of science for policymaking, and the dramatic crisis within science itself. This provocative new volume in The Rightful Place of Science also explores the...