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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
Projects
Publications
Book Chapters
Alston, C.L., Mengersen KL., Pettitt A.N., Alston C. L., Mengersen KL., & Pettitt A.N.
(2012). Case Studies in Bayesian Statistical Modelling and Analysis.
319-320. doi: 10.1002/9781118394472
Conference Papers
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
Friel, N., McKeone J. P., Oates C., & Pettitt A.N.
(2017). Investigation of the widely applicable Bayesian information criterion.
Statistics and Computing. doi: 10.1007/s11222-016-9657-y
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
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
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
Vo, B., Drovandi C. C., Pettitt A.N., Pettet G. J., & 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
Lee, X. Ju, 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
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
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
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
Unpublished Reports
South, L. F., Drovandi C. C., & Pettitt A.N.
(2016). Sequential Monte Carlo for static Bayesian models with independent MCMC proposals.
