Computational Statistics

Theme Overview

This theme is concerned with advancing the theory, methodology, algorithmic development and application of simulation based approaches, such as Markov Chain Monte Carlo, to statistical inference.

Theme Members

Name Keywords
Ricardo Silva   (Theme Lead) Graphical models; Markov chain Monte Carlo; variational methods
Simon Byrne
Bayesian methods; graphical models; Markov chain Monte Carlo
Petros Dellaportas
Markov chain Monte Carlo
Christian Henning Classification; cluster analysis; data visualisation; model selection; multivariate data analysis
Ioannis Kosmidis Categorical data models; clustering methods; modelling of natural disasters; modelling of sport and health tracking and monitoring data; penalized likelihood methods
Ioanna Manolopoulou
Bayesian statistics; diffusion models; mixture modelling; state-space models
Giampiero Marra
Numerical optimisation; penalised likelihood
Yvo Pokern
High-dimensional Gaussian Markov random fields; inference for partially observed and hypoelliptic diffusion processes; nonparametric inference for stochastic differential equations; sequential Monte Carlo methods
Gordon Ross
Bayesian methods; change points; nonparametric statistics; nonstationary processes; point processes
Jinghao Xue  Bootstrapping; data mining; ensemble learning

Other members: Alexandros Beskos, Serge Guillas, Franz Király, Gareth Peters.

Some of our current PhD students are also working on topics related to this theme.

Current and Recent Externally Funded Projects

  • Learning Highly Structured Sparse Latent Variable Models, £100k, EPSRC EP/J013293/1, Oct 2012 - Dec 2013, PI: Silva.
  • Sequential Monte Carlo methods for applications in high dimensions, £99k, EPSRC EP/J01365X/1, Jul 2012 - Jun 2013, PI: Beskos.

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