Dr Christopher SherlockReader
- MCMC theory, methodology and application
- Stochastic processes (hidden Markov models)
- Spatial statistics and veterinary epidemiology
PhD Supervision Interests
Projects are available in combinations of (1) MCMC theory: efficiency as a function of tuning parameters, and convergence; (2) MCMC methodology: developing new algorithms or variations on existing algorithms; (3) MCMC applications in e.g. the environment or veterinary epidemiology; (4) particle filters and bridges for stochastic processes. Example MCMC areas include: pseudo-marginal, delayed acceptance, HMC, non-reversible, and MCMC and variations for tall data.
- Bayesian and Computational Statistics
- STOR-i Centre for Doctoral Training