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Statistical Methodology and Computational Statistics

Statistical Methodology

Statistical Methodology develops new classes of statistical models and new approaches to analysing such new classes. These could be structured high dimensional models, graphical models, non-parametric models and a variety of other classes. Developments of new Bayesian methodologies are numerous and form one of the internationally acclaimed activities at Warwick but there are many other areas of strength as well.

Staff members working in this area include Jim Q. Smith, Chenlei Leng, Murray Pollock, Ioannis Kosmidis, David Wild.

Computational Statistics

Computational Statistics develops and employs computationally intensive techniques to answer statistical questions which might otherwise be difficult or impossible to answer. Such methods often involve the approximate solution of intractable integrals, or optimization problems, via numerical or probabilistic (simulation-based) techniques.
Computational Statistics is very active in Warwick, with interested researchers including Paul Jenkins, Adam M. Johansen, Wilfrid S. Kendall, Krzysztof Łatuszyński, Simon Spencer, Chenlei Leng, Murray Pollock, David Wild, Gareth Roberts, Christian Robert, Theo Damoulas, Ioannis Kosmidis, Jere Koskela, Ritabrata Dutta.


I came to Warwick after completing an MSc in Mathematics in Germany. The Statistics department has a great reputation and various people I had met over the years suggested I come here for my PhD. I am very happy I did so because my experience has been far better than I could ever have imagined!

On the academic side, people in the department come from very diverse backgrounds: there are PhD students who stay on after completing their undergraduate and Master degrees in Statistics but also many who have studied Economics or have already worked in industry, and those like me who come from pure Maths and have only recently developed an interest in applications. My project uses methods from graph theory, algebra, computer science and statistics, and there is always someone to talk to about any of these faces of my research. PhD students can attend a vast range of seminars and reading groups and are also given the opportunity to teach some problem classes.
I have always had all the support I could have wished for: from extra training in Statistics in my first year and very frequent meetings with my supervisor, to regular reports with other members of staff to monitor my progress, and the chance to present my research in a safe environment before travelling to conferences.

On the social side, people are very friendly and very interested in getting to know each other. The SSLC (student and staff liaison committee) is very active in organising Friday pub trips and other events during term time.