Professor of Statistics
University of Nottingham, UK
Theo's research is concerned with the development of novel computational statistical methodology for Bayesian inference and model selection for high-dimensional complex data. The area that he has mostly worked on and made contributions to, is infectious disease modelling. His particular focus is on the design of efficient Monte Carlo methods (e.g. Markov Chain Monte Carlo, Sequential Monte Carlo and Approximate Bayesian Computation), including recent work in developing novel Bayesian Non-Parametrics methodology for epidemic models. More details, including links to pre-prints, can be found here: https://www.maths.nottingham.ac.uk/plp/pmztk/
Professor of Statistics
University of Nottingham, UK
Theo's research is concerned with the development of novel computational statistical methodology for Bayesian inference and model selection for high-dimensional complex data. The area that he has mostly worked on and made contributions to, is infectious disease modelling. His particular focus is on the design of efficient Monte Carlo methods (e.g. Markov Chain Monte Carlo, Sequential Monte Carlo and Approximate Bayesian Computation), including recent work in developing novel Bayesian Non-Parametrics methodology for epidemic models. More details, including links to pre-prints, can be found here: https://www.maths.nottingham.ac.uk/plp/pmztk/