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  4. Statistics
  5. Bayesian statistics

Bayesian statistics

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Bayesian statistics is a major area of endeavour which is at the forefront of practical statistics thanks to Markov Chain Monte Carlo computational methods.

Members of the Bayesian Statistics Research Group work on: Bayesian dynamic graphical models for multivariate forecasting; traffic networks; expert calibration; combination of subjective expert judgement; model selection and model combination; graphical modelling of space-time processes; model uncertainty; dynamic Bayesian modelling of non-linear auto-regressive time series; Bayesian methods for branching processes; applications of Bayesian methodology to infectious diseases; elicitation of prior distributions; misclassified binary data; multivariate meta analysis; Bayesian methodology in ecology, nuclear emergencies and energy; Bayesian networks and forecasting in the aviation industry; and case-control studies.

Qualifications available:

PhD or MPhil


For detailed information on current fees visit Fees and funding.

Entry requirements:

Minimum 2:1 (or equivalent)

Current / recent research projects

Specific projects can be found on the Statistics Group website

Potential supervisors

  • Dr Alvaro Faria
  • Professor Paddy Farrington
  • Professor Paul Garthwaite
  • Professor Kevin McConway
  • Dr Catriona Queen
  • Dr Karen Vines
  • Dr Heather Whitaker

Further information

If you have an enquiry specific to this research area please contact:

Mathematics and Statistics Research Secretary
+44 (0)1908 659995

For general enquiries please contact the Research Degrees Team via the link under ‘Your questions’ on the right of the page.