Prof. James McGree



PhD, Statistics, University of Queensland, Australia. 2008

Research Interests

  • Optimal experimental design
  • Bayesian computational algorithms
  • Applied statistics

Selected research outputs (also see Google Scholar and

  • Senarathne, S. G. J., Overstall, A. M. and McGree, J. M. (2020) Bayesian adaptive N-of-1 trials for estimating population and individual treatment effectsStatistics in Medicine.  Accepted for publication.
  • Overstall, A. M. and McGree, J. M. (2020) Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation. Bayesian Analysis, 15, 103-131.
  • Gedara, J., Drovandi, C. C. and McGree, J. M. (2020) A Laplace-based algorithm for Bayesian adaptive design. Statistics and Computing. Accepted for publication.
  • Cespedes, M. I., McGree, J. M., Drovandi, C. C., Mengersen, K., Fripp, J. and Doecke, J. (2020) Age dependent network dynamics via Bayesian hierarchical models reveal spatio-temporal patterns of neurodegeneration. Statistics in Medicine. Accepted for publication.
  • Gedara, J., Drovandi, C. C. and McGree, J. M. (2019) Bayesian sequential design for Copula models. TEST. Accepted for publication.
  • Firn, J., McGree, J. M., Harvey, E., Schütz, M., Flores, H., Buckley, Y. M., Lind, E. M., Borer, E. T., Seabloom, E. W., La Pierre, K. J., MacDougall, A. M., Prober, S. M., Stevens, C. J., Sullivan, L., Porter, E., LaDouceur, E., Allen, C., Moromizato, K. H., Eisenhauer, N., Wright, J., Arnillas, C. A., Harpole, W. S. and Risch, A.C. (2019) Leaf nutrient contents, but not specific leaf area, increase rapidly and predictably in response to eutrophication. Nature Ecology and Evolution, 3, 400-406.
  • Fisher, R., Shiell, G., Sadler, R., Inostroza, K., Shedrawi, G., Holmes, T. and McGree, J. M. (2019) ‘epower’: an R package for power analysis of Before-After-Control-Impact (BACI) designs. Methods in Ecology and Evolution. Accepted for publication.
  • Dehideniya, M., Drovandi, C. C. and McGree, J. M. (2018) Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology. Computational Statistics & Data Analysis124, 277-297.
  • Overstall, A. M., McGree, J. M. and Drovandi, C. C. (2017) An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions. Statistics and Computing28, 343-358.
  • Drovandi, C. C., Holmes, C., McGree, J. M., Mengersen, K., Ryan, E. and Richardson, S. (2017) Principles of experimental design for big data analysis. Statistical Science32, 385-404.
  • McGree, J. M. (2017) Developments of the total entropy utility function for the dual
    purpose of model discrimination and parameter estimation in Bayesian design. Computational Statistics & Data Analysis113, 207-225.
  • Woods, D. C., McGree, J. M. and Lewis, S. M. (2017) Model selection via Bayesian information capacity designs for generalised linear models. Computational Statistics & Data Analysis, 113, 226-238.
  • McGree, J. M., Drovandi, C. C., White, G. and Pettitt, A. N. (2016) A pseudo-marginal
    sequential Monte Carlo algorithm for random effects models in Bayesian sequential design. Statistics and Computing, 26, 1121-1136.
  • Kang, S. Y., McGree, J. M., Drovandi, C.C., Mengersen, K. and Caley, J. (2016)
    Bayesian adaptive design: Improving the effectiveness of reef monitoring programs. Ecological Applications, 26, 2637-2648.
  • Liu, S., McGree, J. M., Ge, Z. and Xie, Y. (2015). Computational and statistical
    methods for analysing big data with applications. Cambridge University Press.
  • Ryan, E., Drovandi, C. C., McGree, J. M. and Pettitt, A. N. (2016) A review of modern
    computational algorithms for Bayesian optimal design. International Statistical Review, 84, 128-154.
  • Liu, S., Anh, V., McGree, J. M., Kozan, E. and Wolff, R. C. (2015) A new approach
    to the interpolation of complex spatial data. Stochastic Environmental Research and Risk Assessment, 29, 1679-1690.
  • Drovandi, C. C., McGree, J. M. and Pettitt, A. N. (2014) A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design. Journal of Computational and Graphical Statistics, 23, 3-24.
  • Falk, M. G., McGree, J. M. and Pettitt, A. N. (2014) Sampling designs on stream networks using the pseudo-Bayesian approach. Environmental and Ecological Statistics, 21, 751-773.
  • Drovandi, C. C., McGree, J. M. and Pettitt, A. N. (2013) Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data. Computational Statistics & Data Analysis, 57, 320-335.
  • McGree, J. M. and Eccleston, J. A. (2012) Robust designs for Poisson regression models. Technometrics, 54, 64-72.
  • McGree, J. M., Drovandi, C. C., Thompson, H. M., Eccleston, J. A., Duffull, S. B., Mengersen, K., Pettitt, A. N. and T. Goggin (2012) Adaptive Bayesian compound designs for dose finding studies. Journal of Statistical Planning and Inference, 142, 1480-1492.
  • McGree, J. M., Drovandi, C. C. and Pettitt, A. N. (2012) A sequential Monte Carlo approach to design for population pharmacokinetics studies. Journal of Pharmacokinetics and Pharmacodynamics, 39, 519-526.
  • Denman, N. G., McGree, J. M., Eccleston, J. A. and Duffull, S. B. (2011) Design of experiments for bivariate binary responses modelled by Copula functions. Computational Statistics & Data Analysis, 55, 1509-1520.
  • McGree, J. M. and Eccleston, J. A. (2008) Probability-based optimal design. Australian and New Zealand Journal of Statistics, 50, 13-28.
  • McGree, J.M., Duffull, S.B., Ward, L.C. and Eccleston, J.A. “Impedance Measures”, 25 May 2007, Patent AU2007000726.

Selected PhD projects

  • Bayesian design for dependent data
  • Model-based adaptive monitoring: Improving the effectiveness of reef monitoring programs
  • Bayesian adaptive design for monitoring submerged shoals
  • Optimal Bayesian experimental designs for complex models (Completed)
  • Detection of longitudinal brain atrophy patterns consistent with progression towards Alzheimer’s Disease (Completed)
  • Modelling Parkinson Disease using Bayesian Variable Selection and Association Analysis (Completed)
  • Bayesian modelling of breast cancer data (Completed)
  • Resilience of South-East Queensland’s water supply (Completed)
  • Bayesian models for spatio-temporal assessment of disease (Completed)
  • Bayesian survival analysis using gene expression (Completed)
  • Bayesian algorithms with applications (Completed)
  • Optimal experimental design for nonlinear models with pharmacokinetic-pharmacodynamic applications (Completed)

Contact details

  • Location: GP-O514
  • Phone: (+61) 7 3138 2313
  • Email: