Sensitivity Analysis: Gauging the Worth of Scientific Models

Saltelli, A.

ISBN 10: 0471998923 / ISBN 13: 9780471998921

Published by John Wiley & Sons Inc, 2000

About this Item

This work is a guide to the principles behind sensitivity analysis. It suggests suitable methods for particular types of problem, which allows a greater understanding of the entire causal assessment chain. This makes the impact of source uncertainties and framing assumptions more transparent. Editor(s): Saltelli, A.; Chan, K.; Scott, E. M. Series: Wiley Series in Probability and Statistics. Num Pages: 504 pages, Illustrations. BIC Classification: PBT; PBWH; PDE; TBJ. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 254 x 178 x 34. Weight in Grams: 1042. . 2000. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780471998921

Bibliographic Details

• Title: Sensitivity Analysis: Gauging the Worth of Scientific Models
• Publisher: John Wiley & Sons Inc
• Publication Date: 2000
• Language: English
• ISBN 10: 0471998923
• ISBN 13: 9780471998921
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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

· Includes contributions form the leading researchers active in developing strategies for sensitivity analysis

The principles of sensitivity analysis area carefully described, and suitable methods for approaching many types of problems are given. The book introduces the modeller to the entire causal assessment chain, from data to predictions, whilst explaining the impact of source uncertainties and framing assumptions. A 'hitch-hiker's guide' is included to allow the more experienced reader to readily access specific applications.

Modellers from a wide range of disciplines, including biostatistics, economics, environmental impact assessment, chemistry and engineering will benefit greatly form the numerous examples and applications.

About the Author

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).