Problems at the Applied Mathematics / Statistics Interface

Lead Research Organisation: University of Warwick
Department Name: Mathematics

Abstract

Mathematics is the language of science, and 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.The research will facilitate the development of research at the interface between applied mathematics and statistics, both by the study of fundamental theoretical questions, and by their application to problems of importance in science and technology, such as chemical reactions and weather prediction.The work will thus make fundamental progress on theoretical research questions in mathematics and statistics, and will have direct application in a range of applications from the physical sciences and beyond.

Publications

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Mattingly J (2012) Diffusion limits of the random walk Metropolis algorithm in high dimensions in The Annals of Applied Probability

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Mattingly J (2010) Convergence of Numerical Time-Averaging and Stationary Measures via Poisson Equations in SIAM Journal on Numerical Analysis

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Pillai N (2012) Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions in The Annals of Applied Probability

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Pinski F (2012) G-Limit for Transition Paths of Maximal Probability in Journal of Statistical Physics

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Pinski F (2010) Transition paths in molecules at finite temperature in The Journal of Chemical Physics

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Pokern Y (2013) Posterior consistency via precision operators for Bayesian nonparametric drift estimation in SDEs in Stochastic Processes and their Applications