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Uncertainty Analysis with High Dimensional Dependence Modelling

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Kurowicka, Dorota/ Cooke, Roger 作者
John Wiley & Sons Inc
译者
2006-4 出版日期
302 页数
868.00元 价格
HRD
丛书系列
9780470863060 图书编码

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Uncertainty Analysis with High Dimensional Dependence Modelling 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Uncertainty Analysis with High Dimensional Dependence Modelling 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Uncertainty Analysis with High Dimensional Dependence Modelling 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



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Mathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty. Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. In recent years there has been an explosion of interest in uncertainty analysis. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are among the active research areas. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: An up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis. All the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion. Numerous worked examples and applications. Workbook problems, enabling use for teaching. Software support for the examples, using UNICORN - a Windows-based uncertainty modelling package developed by the authors. A website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples. Uncertainty Analysis with High Dimensional Dependence Modelling offers a comprehensive exploration of a new emerging field. It will prove an invaluable text for researches, practitioners and graduate students in areas ranging from statistics and engineering to reliability and environmetrics.

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