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.
|Ricardo Silva (Theme Lead)||Graphical models; Markov chain Monte Carlo; variational methods|
|Francois-Xavier Briol||Bayesian probabilistic numerical methods; control variates; Markov chain Monte Carlo; quasi-Monte Carlo|
|Petros Dellaportas||Markov chain Monte Carlo|
|Samuel Livingstone||Markov chain Monte Carlo; Markov chain theory; Bayesian computation; probabilistic modelling of complex datasets|
|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|
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.