MCMC methods for sampling function space
Applied mathematics is concerned with developing models with predictive capability, and with probing those models to obtain qualitative and quantitative insight into the phenomena being modelled. Statistics is data-driven and is aimed at the development of methodologies to optimize the information derived from data. The increasing complexity of phenomena that scientists and engineers wish to model, together with our increased ability to gather, store and interrogate data, mean that the subjects of applied mathematics and statistics are increasingly required to work in conjunction in order to significantly progress understanding.This article is concerned with a research program at the interface between these two disciplines, aimed at problems in differential equations where profusion of data and the sophisticated model combine to produce the mathematical problem of obtaining information from a probability measure on function space. In this context there is an array of problems with a common mathematical structure, namely that the probability measure in question is a change of measure from a Gaussian. We illustrate the wide-ranging applicability of this structure. For problems whose solution is determined by a probability measure on function space, information about the solution can be obtained by sampling from this probability measure. One way to do this is through the use of Markov chain Monte-Carlo (MCMC) methods. We show how the common mathematical structure of the aforementioned problems can be exploited in the design of effective MCMC methods.
© 2009 EMS Publishing House. We are very grateful to Yalchin Efendiev, Frank Pinski, Gareth Roberts and Jochen Voss for comments on early drafts of this article, and to Efendiev (Figure 4), Pinski (Figure 1) and Voss (Figures 2 and 3) for providing the illustrations in the article.