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Sensitivity Analysis, Hardcover by Saltelli, Andrea (EDT); Chan, Karen (EDT);...
US $50.00
Approximately£37.60
Condition:
Good
A book that has been read, but is in good condition. Minimal damage to the book cover eg. scuff marks, but no holes or tears. If this is a hard cover, the dust jacket may be missing. Binding has minimal wear. The majority of pages are undamaged with some creasing or tearing, and pencil underlining of text, but this is minimal. No highlighting of text, no writing in the margins, and no missing pages. See the seller’s listing for full details and description of any imperfections.
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eBay item number:226146262682
Item specifics
- Condition
- Book Title
- Sensitivity Analysis
- ISBN
- 9780471998921
About this product
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0471998923
ISBN-13
9780471998921
eBay Product ID (ePID)
1712022
Product Key Features
Number of Pages
512 Pages
Language
English
Publication Name
Sensitivity Analysis : Gauging the Worth of Scientific Models
Publication Year
2000
Subject
Probability & Statistics / General
Type
Textbook
Subject Area
Mathematics
Series
Wiley Series in Probability and Statistics Ser.
Format
Hardcover
Dimensions
Item Height
1.3 in
Item Weight
37.1 Oz
Item Length
9.7 in
Item Width
6.8 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
00-023093
Reviews
"The book has a fair price...I think this is a book that everyone who does modeling should buy. It can readily be read piecemeal...so it is ideal for leisurely self-study..." (Technometrics Vol. 42, No. 4 May 2001) "...this book will prove helpful in the solution of many modeling problems." (La Doc Sti, September 2000) "...presents many different sensitivity analysis methodologies and demonstrates their usefulness in scientific research." (Zentralblatt MATH, Vol. 961, 2001/11), "...this book will prove helpful in the solution of many modeling problems." (La Doc Sti, September 2000) "...presents many different sensitivity analysis methodologies and demonstrates their usefulness in scientific research." (Zentralblatt MATH, Vol. 961, 2001/11)
Dewey Edition
21
Series Volume Number
535
Illustrated
Yes
Dewey Decimal
511/.8
Table Of Content
What is Sensitivity Analysis. Hitchhiker's Guide to Sensitivity Analysis. METHODS. Designs of Experiments. Screening Methods. Local Methods. Sampling-Based Methods. Reliability Algorithms: FORM and SORM Methods. Variance-Based Methods. Managing the Tyranny of Parameters in Mathematical Modelling of Physical Systems. Bayesian Sensitivity Analysis. Graphical Methods. APPLICATIONS. Practical Experience in Applying Sensitivity and Uncertainty Analysis. Scenario and Parametric Sensitivity and Uncertainty Analysis in Nuclear Waste Disposal Risk Assessment: The Case of GESAMAC. Sensitivity Analysis for Signal Extraction in Economic Time Series. A Dataless Precalibration Analysis in Solid State Physics. Appplication of First-Order (FORM) and Second-Order (SORM) Reliability Methods: Analysis and Interpretation of Sensitivity Measures Related to Groundwater Pressure Decreases and Resulting Ground Subsidence. One-at-a-Time and Mini-Global Analyses for Characterizing Model Sensitivity in the Nonlinear Ozone Predictions from the US EPA Regional Acid Deposition Model (RADM). Comparing Different Sensitivity Analysis Methods on a Chemical Reactions Model. An Application of Sensitivity Analysis to Fish Population Dynamics. Global Sensitivity Analysis: A Quality Assurance Tool in Environmental Policy Modelling. CONCLUSIONS. Assuring the Quality of Models Designed for Predictive Tasks. Fortune and Future of Sensitivity Analysis. References. Appendix. Index.
Synopsis
Sensitivity analysis is used to ascertain how a given model output depends upon the inupt 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 from the leading researchers active in developing strategies for sensitivity analysis The principles of sensitivity analysis are carefully described, and suitable methods for approaching many types of problems are given. The book introduces the modeller to the entire casual assessment chain, from data to predictions, whilst explaining the impact of source uncertainties and framing assumptions. A hitch-hikers 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 from the numerous examples and applications., Mit Hilfe einer Sensitivitätsanalyse kann man feststellen, wie ein gegebenes Modell von den Eingabeparametern abhängt und unter welchen Umständen es instabil werden könnte. Die gute Modellierungspraxis kommt ohne solche Betrachtungen zukünftig nicht mehr aus! Ausgehend von einer Erläuterung der Grundprinzipien hilft Ihnen dieses Buch, eine Lösung für Ihre Modellierungsprobleme zu finden. Die Auswirkungen von Unsicherheiten oder Annahmen werden dabei besser durchschaubar., 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., 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., 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.
LC Classification Number
QA402.3.S45 2000
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