5 edition of **Global sensitivity analysis** found in the catalog.

Global sensitivity analysis

- 321 Want to read
- 27 Currently reading

Published
**2008** by John Wiley in Chichester, England, Hoboken, NJ .

Written in English

- Sensitivity theory (Mathematics),
- Global analysis (Mathematics),
- Mathematical models.

**Edition Notes**

Includes bibliographical references (p. [279]-285) and index.

Statement | Andrea Saltelli ... [et al.]. |

Contributions | Saltelli, A. 1953- |

Classifications | |
---|---|

LC Classifications | QA402.3 .G557 2008 |

The Physical Object | |

Pagination | x, 292 p. : |

Number of Pages | 292 |

ID Numbers | |

Open Library | OL21040239M |

ISBN 10 | 0470059974 |

ISBN 10 | 9780470059975 |

LC Control Number | 2007045551 |

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The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials. Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications.

For this definition of sensitivity analysis to be of use, it must first be made clear what is meant here by ‘model’, numerical or otherwise, as well as by the terms ‘input’ and ‘output’ which will be used throughout this book. Global Sensitivity Analysis.

The Primer A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications.

The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice Cited by: Global Sensitivity Analysis book. Read reviews from world’s largest community for readers. Complex mathematical and computational models are used in all /5(3). The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications.

The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice. Well written and easy to follow.

The worked examples are very helpful. The authors obviously have their preference to the type of sensitivity analysis to use. I would have liked a more comprehensive treatment of the FAST method- but even so, if you are considering doing a global sensitivity analysis then this should prove to be an invaluable book/5.

Abstract. This chapter makes a review, in a complete methodological framework, of various global sensitivity analysis methods of model output. Numerous statistical and probabilistic tools (regression, smoothing, tests, statistical learning, Monte Carlo, ) aim at determining the model input variables which mostly contribute to an interest quantity depending on model by: Global sensitivity analysis is the process of apportioning the uncertainty in outputs to the uncertainty in each input factor over their entire range of interest.

A sensitivity analysis is considered to be global when all the input factors are varied simultaneously and the sensitivity is evaluated over the entire range of each input factor. Further insights were gained from this analysis by running multivariate regression analysis (a global sensitivity analysis approach [50]) to explore the influence of uncertainties on the result.

The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials. Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls.

Features numerous exercises and solved problems to help illustrate the applications. Global sensitivity analysis (GSA) provides a structured methodology to investigate uncertainty propagation through complex simulation models, and in particular to quantify the relative influence of model input factors, e.g., parameters, on simulation outputs.

the book outlines challenges and future prospects for implementation in earth. The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials.

Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications.5/5(1). For a target chemical dependent on n parameters, the local sensitivity of its concentration to parameters may be first calculated by the brute-force method or the direct method, and used to screen the final list of parameters for global sensitivity analysis.

To estimate the global sensitivity, (n + 1)r parameter values are first selected from. Sensitivity analysis is an important statistical validation technique in Bayesian Modelling. It is used to ascertain how a given model output depends upon or determines its input parameters.

It often carried out to ensure the quality and accuracy of a model and a way of checking the robustness and reliability of assumptions built into a model.

This book is an expository introduction to the methodology of sensitivity analysis of model output. It is primarily intended for investigators, students and researchers that are familiar with mathematical models but are less familiar with the techniques Brand: Springer International Publishing.

GSAT package includes routines for generic global sensitivity analysis. In particular it implements Sobol' analysis and FAST analysis Reviews: Years later the global sensitivity analysis was developed to overcome the limitations of the local sensitivity analysis such as linearity and local variations (Saltelli, ).

Jermé Morio (Morio. Written by the leading names in the field, Global Sensitivity Analysis: The Primer offers an accessible summary of the essential concepts involved in a sound sensitivity analysis. It is a self-contained book allowing the reader to learn about, and practice, sensitivity analysis through the use of many exercises and solved problems.

A review on global sensitivity analysis methods Bertrand Iooss12 and Paul Lemaˆıtre13 1 EDF R&D, 6 Quai Watier - Chatou, France 2 Institut de Math´ematiques de Toulouse, route de Narbonne - Toulouse, France 3 INRIA Sud-Ouest, cours de la lib´eration - Talence, France Abstract.

This chapter makes a review, in a complete methodologi. Global Sensitivity Analysis by Dr. Andrea A. Saltelli,available at Book Depository with free delivery worldwide/5(3). 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.

The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications.

The book: * Provides a self-contained treatment of the subject, allowing readers to learn and practice 5/5(2). This book is a primer in global sensitivity analysis (SA). Its am-bition is to enable the reader to apply global SA to a mathematical or computational model.

It offers a description of a few selected techniques for sensitivity analysis, used for assessing the relative importance of model input factors. These techniques will answer. Sensitivity analysis and bifurcation analysis are closely related to each other. In sensitivity analysis, especially global sensitivity analysis the effects of input parameter spaces on output quantities of interest are studied.

On the other hand, in bifurcation analysis the critical points within feasible regions of parameters are detected where the long-term dynamics changes Author: Raheem Gul, Stefan Bernhard. Global sensitivity analysis (correlated input parameters) A global sensitivity analysis quantifies how much the uncertainty around each input parameter contributes to the output variance.

To perform a global sensitivity analysis when input parameters are correlated, regression-based methods may still be used, but need to be adapted. 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. Advances in Wastewater Treatment presents a compendium of the key topics surrounding wastewater treatment, assembled by looking at the future technologies, and provides future perspectives in wastewater treatment and modelling.

It covers the fundamentals and innovative wastewater treatment processes (such as membrane bioreactors and granular process).

Sensitivity Analysis in Earth Observation Modeling highlights the state-of-the-art in ongoing research investigations and new applications of sensitivity analysis in earth observation modeling. In this framework, original works concerned with the development or exploitation of diverse methods applied to different types of earth observation data or earth observation-based.

Sensitivity Analysis with Calibration of Natural Resource Variables under Climate Change: Comparing Computable General Equilibrium (CGE) and Econometric Frameworks: /ch This chapter compares two quantitative frameworks, namely, Computable General Equilibrium (CGE) and Econometric models to study the impacts of climate changeAuthor: Nilanjan Ghosh, Somnath Hazra.

Global Sensitivity Analysis. The Primer A catalogue record for this book is available from the British Library ISBN Typeset in 10/12 Times by Integra Software Services Pvt.

Ltd, Pondicherry, India Printed and bound in Great Britain by TJ International, Padstow Cornwall. In general, there are two types of sensitivity analysis: (i) local and (ii) global.

Local sensitivity analysis. Local sensitivity analysis evaluates changes in the model outputs with respect to variations in a single parameter input.

31, 32 The input parameters are typically changed one at a time in relatively small increments (e.g., % fold), 33 and the effect of this individual.

This open access book shows how to use sensitivity analysis in demography, by developing mathematical theory, showing examples of application and presenting new methods for individuals, cohorts, and populations, with applications to humans, other animals, and plants.

Traditional sensitivity analysis that can be called local, is applied to a specified solution, say u∗ = f(x∗). The sensitivity of u∗ with respect to the input can be measured using the derivatives (∂f/∂x i) x=x∗. In the global sensitivity approach individual solutions are not consid-ered.

Gómez Delgado and S. Tarantola () “Global sensitivity analysis, GIS and multi-criteria evaluation for a sustainable planning of a hazardous waste disposal site in Spain” International Journal of GIS, 20 (4), pp.

Variance-based sensitivity analysis (often referred to as the Sobol method or Sobol indices, after Ilya M. Sobol) is a form of global sensitivity analysis. Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs.

For example, given a model with two inputs and one. OAT vs Global methods Among practitioners of sensitivity analysis this is a known problem –non OAT approaches are considered too complex to be implemented by the majority of investigators.

Among the global methods: various types of Monte Carlo filtering. ‘meta-modelling’ (smoothing methods or kriging) and variance based methods. 10File Size: KB. Free 2-day shipping. Buy Global Sensitivity Analysis (Hardcover) at 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.

What-If Calculation: Calculations for testing a financial model using different assumptions and scenarios. What-if`calculations enable the forecaster to check the variance in end results for a Author: Will Kenton.

This book is a ‘primer’ in global sensitivity analysis (SA). Its am-bition is to enable the reader to apply global SA to a mathematical or computational model. It offers a description of a few selected techniques for sensitivity analysis, used for assessing the relative importance of model input factors.

These techniques will answerFile Size: KB. Zhou Y, Lu Z and Cheng K () Sparse polynomial chaos expansions for global sensitivity analysis with partial least squares and distance correlation, Structural and Multidisciplinary Optimization,(), Online publication date: 1-JanGlobal sensitivity analysis 3 are assumed to be independent.

More advanced works, listed in the last section, take into account the dependence between components of X (see Kurowicka and Cooke [48] for an introduction to this issue).

Finally, this review focuses on the SA with respect to the global variability of the model output, usually measuredFile Size: 3MB.6. Sensitivity Analysis: from Theory to Practice.

Example 1: a Composite Indicator. Setting the Problem. A Composite Indicator Measuring Countries’ Performance in Environmental Sustainability. Selecting the Sensitivity Analysis Method. The Sensitivity Analysis Experiment and its Results.

Conclusions.