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Professor Anthony Pettitt
Chief Investigator
Queensland University of Technology
Tony Pettitt is a Professor in the School of Mathematical Sciences at QUT. From 2011 to 2015 he was an Australian Research Council (ARC) Professorial Fellow and, prior to that, Head of the School of Mathematics at QUT for over 19 years. He has also had positions with the universities of Nottingham, Loughborough and Lancaster in the UK and with CSIRO.
Tony is a statistician who has made important contributions in applied, computational and theoretical areas. He is well known for his work in Bayesian computational statistics and applied work in infectious diseases in hospitals and neurology, in particular motor neuron disease, working with clinicians in Brisbane hospitals. Other recent applications include statistical methods applied to investigating cancer cell mobility and model uncertainty quantification.
Tony has published over 130 papers in refereed journals, with an h-index of 33 and over 5200 citations (his change-point paper has over 1500 citations). He has supervised over 25 PhDs to completion, served on various committees and panels of the ARC and the National Health and Medical Research Council (NHMRC), and had editorial roles.
Research Interests:
Bayesian statistical computation
Neurology and Motor unit number estimation
Statistical inference for transmission of pathogens and diseases
Qualifications:
PhD (University of Nottingham)
MSc (University of Nottingham)
BSc(Hons) (University of Nottingham)
Projects
Publications
Invited talks, refereed proceedings and other conference outputs
Liquet, B.., Mengersen KL., Pettitt A.N., & Sutton M.
(2018). Bayesian Variable Selection Regression Of Multivariate Responses For Group Data.
Bayesian Statistics in the Big Data Era.
Mengersen, KL., Pettitt A.N., Sutton M., & Liquet B..
(2016). Bayesian Variable Selection Regression Of Multivariate Responses For Group Data.
Australian Statistical Conference.
Vo, B., Drovandi C. C., Pettet G., & Pettitt A.N.
(2016). Approximate Bayesian Computation for cell biology.
Sixth IMS-ISBA Joint Meeting Bayes Comp at MCMski 5.
Journal Articles
L. F. South, Drovandi C. C., & Pettitt A.N.
(2017). Discussion of: A Bayesian information criterion for singular models.
Journal of the Royal Statistical Society, Series B (Statistical Methodology).
Friel, N., McKeone JP., Oates C., & Pettitt A.N.
(2017). Investigation of the widely applicable Bayesian information criterion.
Statistics and Computing. 27, 833–844. doi: 10.1007/s11222-016-9657-y
Liquet, B.., Mengersen KL., Pettitt A.N., & Sutton M.
(2017). Bayesian Variable Selection Regression of Multivariate Responses for Group Data.
Bayesian Analysis. 12(4), 1039 - 1067. doi: 10.1214/17-BA1081
Lee, X. J., Fulford G. R., Pettitt A.N., & Ruggeri F.
(2017). A stochastic model for MRSA transmission within a hospital ward incorporating environmental contamination.
Epidemiology and Infection. 145(4), 825-838. doi: 10.1017/S0950268816002880
Ryan, C. M., Drovandi C. C., & Pettitt A.N.
(2016). Optimal Bayesian experimental design for models with intractable likelihoods using indirect inference applied to biological process models.
Bayesian Analysis. 11(3), 857-883. doi: 10.1214/15-BA977
Drovandi, C. C., Pettitt A.N., & McCutchan R. A.
(2016). Exact and approximate Bayesian inference for low integer-valued time series models with intractable likelihoods.
Bayesian Analysis. 11(2), 325-352. doi: 10.1214/15-BA950
Ryan, E. G., Drovandi C. C., McGree J., & Pettitt A.N.
(2016). A Review of Modern Computational Algorithms for Bayesian Optimal Design.
International Statistical Review. 84(1), 128-154. doi: 10.1111/insr.12107
McGrory, C.A., Pettitt A.N., Titterington D.M., Alston C.L., & Kelly M.
(2016). Transdimensional sequential Monte Carlo using variational Bayes — SMCVB.
Computational Statistics & Data Analysis. 93, 246-254. doi: 10.1016/j.csda.2015.03.006
Drovandi, C. C., Cusimano N., Psaltis S. T. P., Lawson B. A. J., Pettitt A.N., Burrage P., et al.
(2016). Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments.
Journal of The Royal Society Interface. 13(121), doi: 10.1098/rsif.2016.0214
Falk, M. G., Alston C. L., McGrory C. A., Clifford S., Heron E. A., Leonte D., et al.
(2015). Recent Bayesian approaches for spatial analysis of 2-D images with application to environmental modelling.
Environmental and Ecological Statistics. 22(3), 571-600. doi: 10.1007/s10651-015-0311-1
Vo, B., Drovandi C. C., Pettitt A.N., Pettet G., & Byrne H.
(2015). Melanoma Cell Colony Expansion Parameters Revealed by Approximate Bayesian Computation.
PLoS Computational Biology. 11(12), doi: 10.1371/journal.pcbi.1004635
Ryan, E. G., Drovandi C. C., & Pettitt A.N.
(2015). Simulation-based fully Bayesian experimental design for mixed effects models.
Computational Statistics & Data Analysis. 92, 26-39. doi: 10.1016/j.csda.2015.06.007
Drovandi, C. C., Pettitt A.N., & Lee A.
(2015). Bayesian Indirect Inference Using a Parametric Auxiliary Model.
Statistical Science. 30(1), 72-95. doi: 10.1214/14-STS498
Ryan, E., Drovandi C. C., & Pettitt A.N.
(2015). Fully Bayesian Experimental Design for Pharmacokinetic Studies.
Entropy. 17(3), 1063-1089. doi: 10.3390/e17031063
McGree, J., Drovandi C. C., White G., & Pettitt A.N.
(2015). A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design.
Statistics and Computing. 26(5), 1121-1136. doi: 10.1007/s11222-015-9596-z
Lee, X. J., Drovandi C. C., & Pettitt A.N.
(2015). Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets.
Biometrics. 71(1), 198-207. doi: 10.1111/biom.12249
Larue, G. S., Rakotonirainy A., & Pettitt A.N.
(2015). Predicting Reduced Driver Alertness on Monotonous Highways.
IEEE Pervasive Computing. 14(2), 78-85. doi: 10.1109/MPRV.2015.38
Vo, B., Drovandi C. C., Pettitt A.N., & Simpson M. J.
(2015). Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation.
Mathematical Biosciences. 263, 133-142. doi: 10.1016/j.mbs.2015.02.010
Cameron, E., & Pettitt A.N.
(2014). Recursive Pathways to Marginal Likelihood Estimation with Prior-Sensitivity Analysis.
Statistical Science. 29(3), 397-419. doi: 10.1214/13-STS465
Technical reports and unrefereed outputs
L. F. South, Pettitt A.N., Friel N., & Drovandi C. C.
(2017). Efficient Use of Derivative Information within SMC Methods for Static Bayesian Models.
L. F. South, Drovandi C. C., & Pettitt A.N.
(2016). Sequential Monte Carlo for static Bayesian models with independent MCMC proposals.
