Statistical Science


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

Ricardo Silva (Theme Lead)Graphical models; Markov chain Monte Carlo; variational methods
Francois-Xavier BriolBayesian probabilistic numerical methods; control variates; Markov chain Monte Carlo; quasi-Monte Carlo
Petros DellaportasMarkov chain Monte Carlo
Samuel LivingstoneMarkov chain Monte Carlo; Markov chain theory; Bayesian computation; probabilistic modelling of complex datasets
Ioanna ManolopoulouBayesian statistics; diffusion models; mixture modelling; state-space models
Giampiero MarraNumerical optimisation; penalised likelihood
Yvo PokernHigh-dimensional Gaussian Markov random fields; inference for partially observed and hypoelliptic diffusion processes; nonparametric inference for stochastic differential equations; sequential Monte Carlo methods

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

Reading Group

A Computational Statistics Reading Group is available to all interested staff and research students.

Current and Recent Externally Funded Projects

  • Nodes from the Underground: Causal and Probabilistic Approaches for Complex Transportation Networks, £395K, EPSRC EP/N020723/1, June 2016 - July 2019, PI: Silva
  • A multicriterion approach for cluster validation, £98k, EPSRC EP/K033972/1, Jun 2013 - May 2017, PI: Hennig.
  • Information geometry for Bayesian hierarchical models, £238k, EPSRC EP/K005723/1, Mar 2013 - Mar 2016, PI: Byrne.
  • 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.