Statistical Science


Professor Jim Griffin

PositionProfessor of Statistical Science
Phone (external)020 7679 1698
Phone (internal)41698
Email (@ucl.ac.uk)j.griffin
Personal webpagehttps://jimegriffin.github.io/website/
ThemesBiostatistics, Computational Statistics, General Theory and Methodology, Multivariate and High Dimensional Data,

Biographical Details
Image of Professor Jim Griffin

Jim is Professor in the Department of Statistical Science at University College London. He joined the department in January 2019 and previously worked for 12 years at the University of Kent. He is currently an Associate Editor of the journals Statistics and Computing and Bayesian Analysis and has previously served as Associate Editor of Journal of the Royal Statistical Society, Series B and STAT.

Research Interests

Jim is interested in a wide-range of areas in Bayesian statistics. He is particularly interested in the areas of Bayesian nonparametric modelling, high-dimensional regression modelling and time series modelling, and the computational methods needed to estimate these models. He has worked on applications of these methods in biology, ecology, economics, finance, medicine and sports science.

Selected Recent Publications

  • J. E. Griffin, A. S. Buxton, E. Matechou, D. Bormpoudakis and R. A. Griffiths (2020). Modelling environmental DNA data: Bayesian model selection accounting for false positive and false negative probabilities. Journal of the Royal Statistical Society, Series C, 69, 377-392.
  • A. Diana, J. E. Griffin and E. Matechou (2020). A hierarchical dependent Dirichlet process prior for modelling bird migration patterns in the UK. Annals of Applied Statistics, 14, 473-493.
  • J. E. Griffin and F. Leisen (2018). Modelling and computation using NCoRM mixtures for density regression. Bayesian Analysis, 13, 897-916.
  • M. Kalli and J. E. Griffin (2018). Bayesian nonparametric vector autoregressive models. Journal of Econometrics, 203, 267-282.
  • J. E. Griffin and F. Leisen (2017). Compound random measures and their use in Bayesian nonparametrics. Journal of the Royal Statistical Society, Series B, 79, 525-545.
  • J. E. Griffin and P. J. Brown (2017). Hierarchical Shrinkage Priors for Regression Models. Bayesian Analysis. 12. 135-159.
  • J. E. Griffin (2017). Sequential Monte Carlo methods for mixtures with normalized random measures with independent increment priors. Statistics and Computing, 27, 131-145.