Uncertainty Analysis with High Dimensional Dependence Modelling / Wiley Series in Probability and Statistics (PDF)

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.