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
Mattingly J
(2010)
Convergence of Numerical Time-Averaging and Stationary Measures via Poisson Equations
in SIAM Journal on Numerical Analysis
Cotter S
(2010)
Approximation of Bayesian Inverse Problems for PDEs
in SIAM Journal on Numerical Analysis
Cotter S
(2013)
MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster
in Statistical Science
Pokern Y
(2013)
Posterior consistency via precision operators for Bayesian nonparametric drift estimation in SDEs
in Stochastic Processes and their Applications
Agapiou S
(2013)
Posterior contraction rates for the Bayesian approach to linear ill-posed inverse problems
in Stochastic Processes and their Applications
Beskos A
(2011)
Hybrid Monte Carlo on Hilbert spaces
in Stochastic Processes and their Applications
Hairer M
(2011)
Sampling conditioned hypoelliptic diffusions
in The Annals of Applied Probability
Mattingly J
(2012)
Diffusion limits of the random walk Metropolis algorithm in high dimensions
in The Annals of Applied Probability
Pillai N
(2012)
Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions
in The Annals of Applied Probability
Pinski F
(2010)
Transition paths in molecules at finite temperature
in The Journal of Chemical Physics