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
Dashti M
(2011)
Uncertainty Quantification and Weak Approximation of an Elliptic Inverse Problem
in SIAM Journal on Numerical Analysis
Dashti M
(2013)
MAP estimators and their consistency in Bayesian nonparametric inverse problems
in Inverse Problems
Fearnhead P
(2010)
Random-Weight Particle Filtering of Continuous Time Processes
in Journal of the Royal Statistical Society Series B: Statistical Methodology
Hairer M
(2011)
Sampling conditioned hypoelliptic diffusions
in The Annals of Applied Probability
Hairer, M., Stuart, A.M. And Voss, J.
(2011)
The Oxford Handbook of Nonlinear Filtering
Hoang V
(2013)
Complexity analysis of accelerated MCMC methods for Bayesian inversion
in Inverse Problems
Iglesias M
(2013)
Evaluation of Gaussian approximations for data assimilation in reservoir models
in Computational Geosciences
Iglesias M
(2013)
Ensemble Kalman methods for inverse problems
in Inverse Problems
Kessler M
(2012)
Statistical Methods for Stochastic Differential Equations
Lee W
(2011)
Kalman filtering and smoothing for linear wave equations with model error
in Inverse Problems