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.