Statistics
Head of Discipline: Professor Simon Wilson
Our research is focussed around Bayesian inference and decision theory, especially as with regard to streaming data, scalable algorithms (Big Bayes), adaptive utility and sequential decision making. Application areas include ecology, astronomy, social networks, risk and reliability. Recent research highlights of the Discipline include developments in statistical methods to separate the Cosmic Microwave Background (CMB) and reconstructing palaeoclimate.
Faculty members
| Member | Research Interests | Publications |
|---|---|---|
| Houlding, Brett | System reliability prediction modelling and associated decision theory. |  |
| Nipoti, Bernardo | Bayesian nonparametric statistics, species sampling, survival analysis. | |
| O'Regan, Myra | | |
| White, Arthur | Decision science, model based clustering, social network analysis. | |
| Wilson, Simon | Bayesian statistics, statistical reliability, interface of information and communications systems and statistical learning, computationally intensive statistics | |
| Wyse, Jason | Latent Gaussian models, Model based clustering, Bayesian methods, Bayesian model determination, block modelling, changepoint models, application based model development | |
| Zhang, Mimi | Stochastic Modelling, Markov Decision Process, Multivariate Modelling, Data Mining | |
A full list of all members of the discipline can be found here.