# Staff Profile

## Dr Dennis Prangle

### Lecturer in Statistics

- Email: dennis.prangle@ncl.ac.uk
- Telephone: +44 (0) 191 208 7295
- Personal Website: https://dennisprangle.github.io/

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.

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*2020,**12**(1), 7-20. - 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.