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PhD (University of Queensland), Bachelor of Science (University of Queensland)
Research interests:
- Optimal experimental design
- Bayesian computational algorithms
- Big data analytics
Webpage, see here:
Research projects, see here:
Full list of publications, see here:
My book on Big Data, see here:
Research discipline:
Additional information
- Overstall, A. & McGree, J. (2020). Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation. Bayesian Analysis, 15(1), 103–131.
- Senarathne, S., Drovandi, C. & McGree, J. (2020). A Laplace-based algorithm for Bayesian adaptive design. Statistics and Computing, 30(5), 1183–1208. https://eprints.qut.edu.au/131596
- Overstall, A., McGree, J. & Drovandi, C. (2018). An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions. Statistics and Computing, 28(2), 343–358. https://eprints.qut.edu.au/98452
- Dehideniya, D., Drovandi, C. & McGree, J. (2018). Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology. Computational Statistics and Data Analysis, 124, 277–297. https://eprints.qut.edu.au/97824
- McGree, J., (2017). Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design. Computational Statistics and Data Analysis, 113, 207–225. https://eprints.qut.edu.au/86673
- Drovandi, C., Holmes, C., McGree, J., Mengersen, K., Richardson, S. & Ryan, E. (2017). Principles of experimental design for big data analysis. Statistical Science, 32(3), 385–404. https://eprints.qut.edu.au/87946
- McGree, J., Drovandi, C., White, G. & Pettitt, T. (2016). A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design. Statistics and Computing, 26(5), 1121–1136. https://eprints.qut.edu.au/77732
- Ryan, E., Drovandi, C., McGree, J. & Pettitt, T. (2016). A review of modern computational algorithms for Bayesian optimal design. International Statistical Review, 84(1), 128–154. https://eprints.qut.edu.au/75000
- Drovandi, C., McGree, J. & Pettitt, T. (2014). A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design. Journal of Computational and Graphical Statistics, 23(1), 3–24. https://eprints.qut.edu.au/49601
- McGree, J. & Eccleston, J. (2012). Robust designs for Poisson regression models. Technometrics, 54(1), 64–72. https://eprints.qut.edu.au/45857
- Title
- Agri-Intelligence in Cotton Production Systems - Stage 1
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- QUT1701
- Start year
- 2017
- Keywords
- Agriculture Cybernetics; Digital Agriculture; In-Farm Decision Support Systems; Management of Inputs in Agriculture; Value Chain of Cotton Crops
- Title
- Innovating Optimal Experimental Design through Bayesian Statistics
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP120100269
- Start year
- 2012
- Keywords
- Sequential Monte Carlo; Bayesian Adaptive Design; Markov Chain Monte Carlo; Optimal Design; Bayesian Computation
- Bayesian System Identification for Nonlinear Dynamical Vehicle Models (2021)
- Model-Based Adaptive Monitoring: Improving the Effectiveness of Reef Monitoring Programs (2021)
- Experimental Design for Dependent Data (2020)
- Optimal Bayesian Experimental Designs for Complex Models (2019)
- Detection of Longitudinal Brain Atrophy Patterns Consistent with Progression Towards Alzheimer's Disease (2018)
- Statistical methods for modelling falls and symptoms progression in patients with early stages of Parkinson's disease (2018)
- Bayesian Statistical Models for Understanding Health-Related Outcomes for Women Screened for Breast Cancer (2016)
- Bayesian models for spatio-temporal assessment of disease (2014)
- Bayesian Survival Analysis Using Gene Expression (2013)
- Bayesian Algorithms with Applications (2012)