Professor James McGree
Faculty of Science,
School of Mathematical Sciences
Biography
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: 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
Discipline
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, (2020) Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation, Bayesian Analysis, 15 (1) p103
- Senarathne S, Drovandi C, McGree J, (2020) A Laplace-based algorithm for Bayesian adaptive design, Statistics and Computing, 30 (5) p1183
- 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) p343
- 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 () p277
- 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 () p207
- 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) p385
- 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) p1121
- Ryan E, Drovandi C, McGree J, Pettitt T, (2016) A review of modern computational algorithms for Bayesian optimal design, International Statistical Review, 84 (1) p128
- 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) p3
- McGree J, Eccleston J, (2012) Robust designs for Poisson regression models, Technometrics, 54 (1) p64
QUT ePrints
To find publications by James, visit QUT ePrints, the University's research repository.
Research projects
- 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
Supervision
Completed supervisions (Doctorate)
- Assessing Uncertainty in Relation to Urban Stormwater Pollutant Processes (2016)
- Human Health Risk of Toxic Chemical Pollutants Generated from Traffic and Land Use Activities (2016)
- Dynamics of Nitrogen and Suspended Solids Removal in Experimental Stormwater Biofilters Under Intermittent Wetting and Drying (2015)
- Bayesian models for spatio-temporal assessment of disease (2014)
- Resilience of Water Supply Systems in Meeting the Challenges Posed by Climate Change and Population Growth (2014)
- Bayesian Survival Analysis Using Gene Expression (2013)
- Bayesian Algorithms with Applications (2012)