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Download Sensitivity Analysis fb2, epub

by Andrea Saltelli,K. Chan,E. M. Scott

Download Sensitivity Analysis fb2, epub

ISBN: 0470743824
Author: Andrea Saltelli,K. Chan,E. M. Scott
Language: English
Publisher: Wiley; 1 edition (March 16, 2009)
Pages: 494
Category: Mathematics
Subcategory: Science
Rating: 4.5
Votes: 373
Size Fb2: 1682 kb
Size ePub: 1205 kb
Size Djvu: 1617 kb
Other formats: txt docx lrf lit


Sensitivity Analysis. Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters.

Sensitivity Analysis. E. M. Scott University of Glasgow, UK. 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.

Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters.

Global Sensitivity Analysis: An Introduction. Eds. 2000, a multi-author book. Saltelli, . K. Chan and M. Scott, Ed. 2000, Handbook of Sensitivity Analysis, John Wiley & Sons publishers, Probability and Statistics series. Abstract: This presentation aims to introduce global sensitivity analysis (SA), targeting an audience unfamiliar with the topic, and to give practical hints about the associated advantages and the effort needed. Prescriptions have been issued for sensitivity analysis of models when these used for policy analysis. 1999, Sensitivity analysis.

Saltelli, . In Sensitivity Analysis; Chan, . Scott, . Ed. John Wiley & Sons, Chichester, 2000. J. Haug, K. Choi and V. Komkov, Design Sensitivity Analysis of Structural Systems, Academic Press, New York, NY, 1986. 29. Turányi, . Rabitz, H. John Wiley & Sons, Chichester, 2000; p 81. 30. Varma, . Morbidelli . Wu H. In Parametric Sensitivity in Chemical Systems; Cambridge, 1999. 48. Kleiber, H. Antunez, T. D. Hien and P. Kowalczyk, Parameter Sensitivity in Nonlinear Mechanics: Theory and Finite Element Computations, John Wiley & Sons, New York, NY, 1997. Andrea Saltelli, K. Chan, E. Scott . 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.

Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should be run in tandem.

Sensitivity Analysis book.

Saltelli . Vidoni . Mascherini M. (2009) Recommended practices in global sensitivity analysis.

forthcoming)Google Scholar. Tarantola, . Campolongo, . and Ratto, . 2004, Sensitivity Analysis in Practice. A Guide to Assessing Scientific Models, John Wiley and Sons, Chichester, New York. Saltelli . Laba . Mysiak J. (eds) Uncertainties in Environmental Modelling and Consequences for Policy Making. NATO Science for Peace and Security Series C: Environmental Security.

Books by A Saltelli with Solutions. Chan, Andrea Saltelli, K. Chan, Scott Bernard, E. Scott, A. Saltelli, E. Marian Scott. Stefano Tarantola, Francesca Campolongo, Marco Ratto, A. Saltelli, Andrea Saltelli. Join Chegg Study and get: Guided textbook solutions created by Chegg experts.

The Wiley Paperback Series makes valuable content more accessibleto a new generation of statisticians, mathematicians andscientists.

Sensitivity analysis is used to ascertain how a given modeloutput depends upon the input parameters. This is an importantmethod for checking the quality of a given model, as well as apowerful tool for checking the robustness and reliability of itsanalysis. The topic is acknowledged as essential for good modellingpractice 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 andcase studies.Includes contributions from the leading researchers active indeveloping strategies for sensitivity analysis.

The principles of sensitivity analysis are carefully describedand suitable methods for approaching many types of problems aregiven. The book introduces the modeller to the entire casualassessment chain, from data to predictions, whilst explaining theimpact of source uncertainties and framing assumptions. A‘hitch-hikers guide’ is included to allow the moreexperienced reader to readily access specific applications.

Modellers from a wide range of disciplines, includingbiostatistics, economics, environmental impact assessment,chemistry and engineering will benefit greatly from the numerousexamples and applications.

"Presents many different sensitivity analysis methodologies anddemonstrates their usefulness in scientific research."(Zentralblatt MATH)

Comments:

Haracetys
The best book on this topic (for professionals)!
Bele
Too often modellers do not realise that sensitivity analysis is an essential part of the model building process. This volume has a didactical value showing how SA is often useful - and sometimes essential- to complete the model building process and to interpret results properly. It guides the reader through an array of different approaches, illustrating in a generally clear fashion the specificity of the different techniques to different problem-setting.
Although this is a multi-authored book, the discourse flows clearly across (most of) the chapters and coveys the main element of this new discipline.
The authors-editors show an overall preference for sensitivity analysis methods capable of global quantitative sensitivity analysis; the sections of the book devoted to local methods and to regression analysis are rather a useful review than actually new material. The sections on variance-based methods and on high dimensional model representations are probably the most instructive for the educated reader.
The applications are in general well presented and instructive. These range from atmospheric chemistry to material physics. A chapter on available software is also offered. Finally the chapter from Beck and Chen (Assuring The Quality Of Models Designed For Predictive Tasks) establishes the needed link between the present raging debate on model validation and the use of adequate sensitivity analysis methods.
Went Tyu
This book provides introduction to theoretical background and first "hands on" practical computations in sensitivity analysis. Sensitivity analysis is based on systems view of statistical evaluation in quantification and extraction of effects of input factors in multivariate, nonlinear and dynamical systems on system output performance. The authors review essentials of theoretical approaches and algorithms through several simple small scale problems with paradigm notions. At the same time, the book serves as a guide for use of freely available software SimLab provided by the same authors.
The book is highly recommendable to all researchers involved in modeling in a very broad range of complex systems, such as in economy, environmental studies, biology, medical engineering, chemistry etc.

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