Professor James McGree
Faculty of Science,
School of Mathematical Sciences
Biography
Research interests:- Optimal experimental design
- Bayesian computational algorithms
- Big data analytics
Research projects, see here:
Full list of publications, see here:
My book on Big Data, see here:
Research discipline:
Mathematical Sciences
Personal details
Positions
- Professor in Statistics
Faculty of Science,
School of Mathematical Sciences
Keywords
Optimal experimental design, Bayesian design, Sequential design, Adaptive design, Bayesian modelling, Bayesian computational algorithms
Research field
Statistics
Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2008
Qualifications
- PhD (University of Queensland)
- Bachelor of Science (University of Queensland)
Publications
- Overstall, A. & McGree, J. (2022). Bayesian Decision-Theoretic Design of Experiments Under an Alternative Model. Bayesian Analysis, 17(4), 1021–1041. https://eprints.qut.edu.au/240029
- 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. (2020). Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation. Bayesian Analysis, 15(1), 103–131. https://eprints.qut.edu.au/150900
- 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
- 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
- Eccleston, J. & McGree, J. (2012). Robust designs for Poisson regression models. Technometrics, 54(1), 64–72. https://eprints.qut.edu.au/45857
QUT ePrints
For more publications by James, explore their research in QUT ePrints (our digital repository).
Selected research projects
- Title
- A precision medicine clinical trial platform to BEAT CF
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- 2014916
- Start year
- 2023
- Keywords
- cystic fibrosis; cystic fibrosis transmembrane regulator (CFTR); clinical trial; respiratory medicine; complex genetic disease
- Title
- Repurposing Existing Medications to Reduce Severe Acute Respiratory Distress in Patients with COVID-19: the CLARITY trial
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- 2002277
- Start year
- 2022
- Keywords
- respiratory viruses; renin-angiotensin system (RAS); acute respiratory distress syndrome (ARDS); angiotensin receptor; anti-hypertensive therapy
- Title
- Precision Ecology: The Modern Era of Designed Experiments in Plant Ecology
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP200101263
- Start year
- 2020
- Keywords
- Title
- Managing Complex Networks in Endangered Grasslands to Restore Food Webs
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP190100500
- Start year
- 2019
- Keywords
- 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
Projects listed above are funded by Australian Competitive Grants. Projects funded from other sources are not listed due to confidentiality agreements.
Supervision
Completed supervisions (Doctorate)
- 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)