Google scholar citations


  1. Holden, P., Edwards, N., Ridgwell, A., Wilkinson, R., Fraedrich, K., Lunkeit, F., … and Jorge Vinuales. (2018). Climate-carbon cycle uncertainties and the Paris Agreement. Nature Climate Change.
  2. Holden, P., Edwards, N., & Wilkinson. (2018). ABC for climate: dealing with expensive simulators. In The Handbook of ABC.
  3. Quantifying Age and Model Uncertainties in Paleoclimate Data and Dynamical Climate Models with a Joint Inferential Analysis. (2018). In Submission.


  1. Carson, J., Crucifix, M., Preston, S., & Wilkinson. (2017). Bayesian model selection for the glacial-interglacial cycle. To Appear, Journal of the Royal Statistical Society.
  2. Uteva, E., Graham, R., Wilkinson, & Wheatley, R. (2017). Interpolation of intermolecular potentials using Gaussian processes. Journal of Chemical Physics, 147, 161706. Selected as a 2017 Editors’ Choice article
  3. Tian, L., Wilkinson, Yang, Z., Power, H., Fargerlund, F., & Niemi, A. (2017). Gaussian Process Emulators for Quantifying Uncertainty in CO2 Spreading Predictions in Heterogeneous Media. Computers and Geosciences, 105, 113–119.


  1. Cresswell, A. J., Wheatley, R. J., Wilkinson, R. D., & Graham, R. S. (2016). FDCCS16 Molecular simulation of the thermophysical properties and phase behaviour of impure CO 2 relevant to CCS. Faraday Discussions.
  2. Wilkinson, Kapranas, A., & Hardy, I. C. W. (2016). Statistical methods for detecting non-binomial sex allocation when developmental mortality operates. Journal of Theoretical Biology, 408, 167–178.
  3. Crevillén-García, D., Wilkinson, R. D., and A. A. Shah, & Power, H. (2016). Gaussian Process Modelling for Uncertainty Quantification in Convectively-Enhanced Dissolution Processes in Porous Media. To Appear, Advances in Water Resources.
  4. Wilkinson. (2016). A discussion of The impact of structural error on parameter constraint in a climate model, by D McNeall, J. Williams, B. Booth, R. Betts, P. Challenor, A. Wiltshire, and D. Sexton. Earth System Dynamics.
  5. Wilkinson. (2016). A discussion of Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model, by D Williamson et al. Geoscientific Model Development Discussion.


  1. Holden, P. B., Edwards, N. R., Garthwaite, P. H., & Wilkinson. (2015). Emulation and interpretation of high-dimensional climate model outputs. Journal of Applied Statistics, 42, 2038–2055.
  2. Bounceur, N., Crucifix, M., & Wilkinson. (2015). Global sensitivity analysis of the climate vegetation system to astronomical forcing: an emulator-based approach. Earth Syst. Dynam. Discuss, 6, 205–224.


  1. Wilkinson. (2014). A discussion of PLASIM-ENTSem: a spatio-temporal emulator of future climate change for impacts assessment, by P.B. Holden, N. R. Edwards, P. H. Garthwaite, K. Fraedrich, F. Lunkeit, E. Kirk, M. Labriet, A. Kanudia, and F. Babonneau. Geoscientific Model Development, 7, 433–451.
  2. Bracken-Grissom, H. D., Ahyong, S. T., Wilkinson, R. D., Feldmann, R. M., Schweitzer, C. E., Breinholt, J. W., … others. (2014). The emergence of lobsters: phylogenetic relationships, morphological evolution and divergence time comparisons of an ancient group (Decapoda: Achelata, Astacidea, Glypheidea, Polychelida). Systematic Biology, 63(4), 457–479.
  3. Wilkinson. (2014). Accelerating ABC methods using Gaussian processes. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, 33, 1015–1023.


  1. Wilkinson. (2013). Approximate Bayesian computation (ABC) gives exact results under the assumption of model error. Statistical Applications in Genetics and Molecular Biology, 12, 129–142.


  1. Wilkinson. (2012). A short discussion of Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation by Fearnhead and Prangle. Journal of the Royal Statistical Society, Series B.


  1. Wilkinson, R. D., M., V., Cornford, D., & E., O. J. (2011). Quantifying simulator discrepancy in discrete-time dynamical simulators. Journal of Agricultural, Biological and Environmental Statistics, 16(4), 554–570. There is also some supplementary material available here
  2. Ricciuto, D. M., Tonkonojenkov, R., Urban, N., Wilkinson, Matthews, D., Davis, K. J., & Keller, K. (2011). Assimilation of global carbon cycle observations into an Earth system model to estimate uncertain terrestrial carbon cycle parameters.
  3. Wilkinson, R. D., Steiper, M. E., Soligo, C., Martin, R. D., Yang, Z., & Tavaré, S. (2011). Dating primate divergences through an integrated analysis of palaeontological and molecular data. Systematic Biology, 60(1), 16–31.

2010 and earlier

  1. Wilkinson, R. D. (2010). Bayesian calibration of expensive multivariate computer experiments. In L. T. Biegler, G. Biros, O. Ghattas, M. Heinkenschloss, D. Keyes, B. K. Mallick, … Y. Marzouk (Eds.), Large-scale inverse problems and quantification of uncertainty. John Wiley and Sons.
  2. Holden, P. B., Edwards, N. R., Oliver, K. I. C., Lenton, T. M., & Wilkinson, R. D. (2010). A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1. Climate Dynamics, 35(5), 785–806.
  3. Bastos, L., & Wilkinson, R. D. (2010). Análise Estatística de Simuladores (Statistical Analysis of Computer Experiments). Simpósio Nacional de Probabilidade e Estatística 19o (SINAPE).
  4. Wilkinson, R. D., & Tavaré, S. (2009). Estimating primate divergence times by using conditioned birth-and-death processes. Theoretical Population Biology, 75(4), 278–285.
  5. Wilkinson. (2008). Bayesian inference of primate divergence times (PhD thesis). Department of Applied Mathematics and Theoretical Physics, University of Cambridge.