Bayesian uncertainty analysis for complex physical systems modelled by computer simulators

Michael Goldstein
Durham University, UK

Most large and complex physical systems are studied by mathematical models, implemented as high dimensional computer simulators.  While all such cases differ in physical description, each analysis of a physical system based on a computer simulator involves the same underlying sources of uncertainty. There is a growing field of study which aims to quantify and synthesise all of the uncertainties involved in relating models to physical systems, within the framework of Bayesian statistics, and to use the resultant uncertainty specification to address problems of forecasting and decision making based on the application of these methods. This talk will give an overview of aspects of this emerging methodology, with particular emphasis on Bayesian emulation, structural discrepancy modelling and iterative history matching. The methodology will be illustrated with examples of current areas of practical application.