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- Anthony Pettitt
Professor Anthony Pettitt
Chief Investigator
Queensland University of Technology
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
Prizes, awards and special recognition
2018
The Lindley Prize was awarded to Matt Sutton, Benoit Liquet, Kerrie Mengersen, Anthony Pettitt. Awarded from the International Society for Bayesian Analysis.
The Lindley Prize is awarded for innovative research in Bayesian Statistics that is presented at an ISBA World Meeting and accepted for publication in the journal Bayesian Analysis.
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
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
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
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.
