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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
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
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
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
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
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
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
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
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
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.
