Könyv Uncertainty Analysis with High Dimensional Dependence Modelling R. Cooke

Uncertainty Analysis with High Dimensional Dependence Modelling

Nyelv: Angol
Kötés: Kemény kötésű
Elérhetőség: 50 % esély
Keressük az egész világon
46 388 Ft
Mathematical models are used to simulate complex real-world phenomena in many areas of science and t...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2006
oldal
302
EAN
9780470863060
ISBN
0470863064
Enbook ID
04886465
Súly
564
Méretek
165 x 237 x 23

Teljes leírás

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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