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)
  

• Prangle D, Everitt RG, Kypraios T. A rare event approach to high-dimensional approximate Bayesian computation. Statistics and Computing 2018, 28(4), 819-834.
• Ryder T, Golightly A, McGough AS, Prangle D. Black-box Variational Inference for Stochastic Differential Equations. In: 35th International Conference on Machine Learning (ICML 2018). 2018, Stockholm, Sweden: International Machine Learning Society.
• Prangle D. gk: An R Package for the g-and-k and Generalised g-and-h Distributions. The R Journal 2018. In Press.
• Rodrigues GS, Prangle D, Sisson SA. Recalibration: A post-processing method for approximate Bayesian computation. Computational Statistics and Data Analysis 2018, 126, 53-66.
• Prangle D. Summary statistics in Approximate Bayesian Computation. In: Sisson S; Fan Y; Beaumont M, ed. Handbook of Approximate Bayesian Computation. London: Chapman and Hall/CRC, 2018.
• van der Vaart E, Prangle D, Sibly R. Taking Error Into Account When Fitting Models Using Approximate Bayesian Computation. Ecological Applications 2018, 28(2), 267-274.
• Kypraios T, Neal P, Prangle D. A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation. Mathematical Biosciences 2017, 287, 42-53.
• Prangle D. Adapting the ABC distance function. Bayesian Analysis 2017, 12(1), 289-309.
• Prangle D. Lazy ABC. Statistics and Computing 2016, 26(1), 171-185.
• Nunes MA, Prangle D. abctools: an R package for tuning approximate Bayesian computation analyses. The R Journal 2015, 7(2), 189-205.
• Falys CG, Prangle D. Estimating age of mature adults from the degeneration of the sternal end of the clavicle. American Journal of Physical Anthropology 2015, 156(2), 203-214.
• Prangle D, Blum MGB, Popovic G, Sisson SA. Diagnostic tools for approximate Bayesian computation using the coverage property. Australian and New Zealand Journal of Statistics 2014, 56(4), 309-329.
• Blum MGB, Nunes M, Prangle D, Sisson SA. A comparative review of dimension reduction methods in approximate Bayesian computation. Statistical Science 2013, 28(2), 189-208.
• Prangle D, Fearnhead P, Cox MP, Biggs PJ, French NP. Semi-automatic selection of summary statistics for ABC model choice. Statistical Applications in Genetics and Molecular Biology 2013.
• Fearnhead P, Prangle D. Constructing summary statistics for approximate Bayesian computation; semi-automatic ABC. Journal of the Royal Statistical Society (series B) 2012.