School of Mathematics, Statistics and Physics

Staff Profile

Dr Dennis Prangle

Lecturer in Statistics


I'm a statistics lecturer with research interests in computational Bayesian statistics and machine learning.

My office is 2.13 in the Herschel building.

For full details see my CV.


My research focuses on likelihood-free methods for statistical inference, and in particular Approximate Bayesian Computation (ABC). Likelihood-free methods are used for complex models where the likelihood function and associated traditional methods of inference are unavailable. Instead they exploit repeated simulations from the model to learn about its parameters. Current research areas include improving the efficiency of existing methods and developing new approaches which scale to bigger datasets/models, for example by iteratively improving simulations and utilising machine learning methods.

Areas of application include:

  • Population genetics
  • Infectious disease epidemics
  • Agent based ecological models
  • Finance models incorporating the possibility of extreme events
  • Environmental extremes
  • Stochastic differential equations

I'm very happy to discuss research projects on any likelihood-free methods or applications, as well as general Bayesian and computational statistics.

Google Scholar page


In 2017/2018 I'm teaching:

  • Statistical Inference (MAS3905/8905)
  • Introduction to Bayesian Statistics (MAS2903)