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.
Organisations
People |
ORCID iD |
Andrew Stuart (Principal Investigator) |
Publications
Lee W
(2011)
Kalman filtering and smoothing for linear wave equations with model error
in Inverse Problems
Cotter S
(2011)
Variational data assimilation using targetted random walks
in International Journal for Numerical Methods in Fluids
Stuart A
(2012)
Evaluating Data Assimilation Algorithms
in Monthly Weather Review
Schwab C
(2012)
Sparse deterministic approximation of Bayesian inverse problems
in Inverse Problems
Stuart A
(2012)
Besov priors for Bayesian inverse problems
in Inverse Problems and Imaging
Pinski F
(2012)
G-Limit for Transition Paths of Maximal Probability
in Journal of Statistical Physics
Mattingly J
(2012)
Diffusion limits of the random walk Metropolis algorithm in high dimensions
in The Annals of Applied Probability
Kessler M
(2012)
Statistical Methods for Stochastic Differential Equations
Pillai N
(2012)
Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions
in The Annals of Applied Probability
Nolen J
(2012)
Numerical Analysis of Multiscale Problems