106. I Botha, R Kohn, L South, C Drovandi (2023). Automatically adapting the number of state particles in SMC^2. To appear in Statistics and Computing. [pdf]
105. JJ Bon et al (2023). Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.  381:20220156 [pdf]
104. L Davies, R Salomone, M Sutton, C Drovandi (2023). Transport Reversible Jump Proposals. To appear in AISTATS. [pdf]
103. A Chakraborty, DJ Nott, C Drovandi, DT Frazier, SA Sisson (2023). Modularized Bayesian analyses and cutting feedback in likelihood-free
inference. Statistics and Computing. 33:33. [pdf]
102. D Li, A Clements and C Drovandi (2023). A Bayesian approach for more reliable tail risk forecasts. Journal of Fincancial Stability. 64: 101098. [link, preprint]
101. C Edwards et al (2022). Shear wave velocity measurement of the placenta is not limited by placental location. Placenta. 131:23-27 [link]
100. J Roots et al (2022) Variability of Biceps Muscle Stiffness Measured using Shear Wave Elastography at Different Anatomical Locations with Different Ultrasound Machines. Ultrasound in Medicine and Biology. To appear [link]
99. DJ VandenHeuvel, C Drovandi, MJ Simpson  (2022). Computationally efficient mechanism discovery for cell invasion with uncertainty quantification. PLOS Computational Biology. 18: e1010599. [pdf]
98. AP Browning, C Drovandi, IW Turner, AL Jenner, MJ Simpson (2022) Efficient inference and identifiability analysis for differential equation models with random parameters. PLOS Computational Biology.  To appear [pdf]
97. SA Vollert, C Drovandi, GM Monsalve-Bravo, MP Adams (2022) Strategic model reduction by analysing model sloppiness: a case study in coral calcification. Environmental Modelling & Software.  To appear [link, pdf]
96. J Osborne et al (2022) Evidence that heat acclimation training may alter sleep and incidental activity. European Journal of Sports Science. To appear [pdf]
95. S Hu et al (2022) Predictions of Machine Learning with Mixed-effects in Analyzing Longitudinal Data under Model Misspecification. Statistical Methods and Applications.  To appear.  [pdf]
94. LF South, CJ Oates, A Mira and C Drovandi  (2022) Regularized Zero-Variance Control Variates. Bayesian Analysis.  To appear [pdf]
93. GM Monsalve-Bravo et al  (2022) Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data. Science Advances.  8(38) [pdf]
92. T Newans, P Bellinger, C Drovandi, S Buxton and C Minahan (2022) The Utility of Mixed Models in Sport Science: A Call for Further Adoption in Longitudinal Data Sets. International Journal of Sports Physiology and Performance.  17(8):1289-1295 [link]
91. DT Frazier, DJ Nott, C Drovandi and R Kohn (2022) Bayesian inference using synthetic likelihood: asymptotics and adjustments. Journal of the American Statistical Association.  To appear [pdf]
90. AP Browning, N Ansari, C Drovandi, APR Johnson, MJ Simpson and AL Jenner (2022) Identifying cell-to-cell variability in internalization using flow cytometry. Journal of the Royal Society Interface. 19(190):20220019 [pdf]
89. C Drovandi and DT Frazier  (2022) A comparison of likelihood-free methods with and without summary statistics. Statistics and Computing. 32:42 [pdf]
88. HS Murray, C Drovandi, EJ Carr, P Corry (2022) Statistical modelling of goalkicking performance in the Australian football league. Journal of Science and Medicine in Sport. 25(8):690-695  [link]
87. Jones et al (2022) Bronchiectasis - Exercise as Therapy (BREATH): rationale and study protocol for a multi-center randomized controlled trial. Trials. 23:292 [pdf]
86. Le et al (2022) Framework for assessing and easing global COVID-19 travel restrictions. Scientific Reports. 6985 [pdf]
85. Markus Hainy, David J. Price, Olivier Restif, C Drovandi (2022) Optimal Bayesian design for model discrimination via classification. Statistics and Computing. 32:25  [pdf]
84. Doohan et al (2022) Modified Stroop Task Performance When Wearing Protective Clothing in the Heat: An Evaluation of the Maximum Adaptability Model. Physiology and Behavior. 246:113690 [link] 
83. JW Priddle, SA Sisson, DT Frazier, I Turner and C Drovandi (2022) Efficient Bayesian synthetic likelihood with whitening transformations. Journal of Computational and Graphical Statistics.  31(1):50-63 [pdf]
82. DJ Warne, SA Sisson and C Drovandi (2022) Vector Operations for Accelerating Expensive Bayesian Computations – A Tutorial Guide. Bayesian Analysis. 17(2):593-622 [pdf]
81. PP-Y Wu et al (2022) Bayesian prediction of winning times for elite swimming events.  Journal of Sports Science. 40:24-31 [link]
80. Z An, LF South and C Drovandi (2022) BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood.  Journal of Statistical Software.  101:1-33. [pdf]
79. C Drovandi, RG Everitt, A Golightly and D Prangle (2022) Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter. Bayesian Analysis. 17:223-260 (pdf)
78. DT Frazier and C Drovandi (2021) Robust Approximate Bayesian Inference with Synthetic Likelihood. Journal of Computational and Graphical Statistics. 30(4): 958-976  [pdf, link]
77. NE Papanikolaou, T Kypraios, H Moffat, A Fantinou, DP Perdikis and C Drovandi (2021) Predators' Functional Response: Statistical Inference, Experimental Design, and Biological Interpretation of the Handling Time. Frontiers in Ecology and Evolution.  9:740848 [link]
76. C Edwards et al (2021) Changes in placental elastography in the third trimester - analysis using a linear mixed effect model.  Placenta.  114, 83-89 [link]
75. MJ Carr, MJ Simpson and C Drovandi (2021) Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using approximate Bayesian computation​. Journal of the Royal Society Interface. 18, 20210362 [pdf]
74. JJ Bon, A Lee and C Drovandi (2021) Accelerating sequential Monte Carlo with surrogate likelihoods​. Statistics and Computing. 31, 62. [pdf, link]
73. J Camps, B Lawson, C Drovandi, A Minchole, ZJ Wang, V Grau, K Burrage and B Rodriguez (2021) Inference of ventricular activation properties from non-invasive electrocardiography. Medical Image Analysis.  73, 102143. [pdf, link]
72. PP-Y Wu et al (2021) Predicting performance in 4 x 200-m freestyle swimming relay events.  PLoS ONE. 16(7),
e0254538 [pdf]
71. MJ Simpson, AP Browning, C Drovandi, EJ Carr, OJ Maclaren and RE Baker (2021) Profile likelihood analysis for a stochastic model of diffusion in heterogeneous media.  Proceedings to the Royal Society A. 477, 20210214. [pdf]
70. C Drovandi, DJ Nott, and DE Pagendam (2021) A Semi-Automatic Method for History Matching using Sequential Monte Carlo.  SIAM/ASA Journal of Uncertainty Quantification. 9, 1034–1063. [pdf, link]
69. PG Campbell, IB Stewart, AC Sirotic, C Drovandi, BH Foy, GM Minett (2021) Analysing the predictive capacity and dose-response of wellness in load monitoring. Journal of Sports Sciences. 39, 1-9 [link, pdf]
68. MJ Davoudabadi, D Pagendam, C Drovandi, J Baldock and G White (2021) Advanced Bayesian approaches for state-space models with a case study on soil carbon sequestration. Environmental Modelling and Software. 136, 104919 [link, pdf]
67. I Botha, R Kohn and C Drovandi (2021) Particle Methods for Stochastic Differential Equation Mixed Effects Models. Bayesian Analysis. 16, 575-609 [pdf]
66. S Kleinegesse, C Drovandi, MU Gutmann (2021) Sequential Bayesian Experimental Design for Implicit Models via Mutual Information. Bayesian Analysis. 16, 773-802 [pdf]
65. D Li, A Clements and C Drovandi (2021) Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo.  Econometrics and Statistics. 19, 22-46 [pdf, link]
64. KS Wilson-Stewart, D Fontanarosa, D Li, C Drovandi, RK Anderson and JV Trapp (2020) Taller staff occupationally exposed to less radiation to the temple in cardiac procedures, but risk higher doses during vascular cases. Scientific Reports. 10, 16103 [pdf]
63. DJ Warne, A Ebert, C Drovandi, W Hu, A Mira and K Mengersen (2020) Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic. BMC Public Health. 20, 1868  [pdf]
62. DN Borg et al (2020) The effect of access to a designated interdisciplinary post-acute rehabilitation service on participant outcomes after brain injury. Brain Injury. 34, 1358-1366 [link]
61. MI Cespedes, J McGree, C Drovandi, K Mengersen, J Fripp, JD Doecke  (2020) Relative rate of change in cognitive score network dynamics via Bayesian hierarchical models reveal spatial patterns of neurodegeneration​. Statistics and Medicine. 39, 2695-2713  [link]
60. A Varghese, C Drovandi, K Mengersen and A Mira (2020) Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation​. PLOS Computational Biology. 16(5), e1007878 [pdf]
59. H Moffat, M Hainy, NE Papanikolaou and C Drovandi (2020) Sequential Experimental Design for
Predator-Prey Functional Response Experiments. Journal of the Royal Society Interface. 17, 20200156 [​link, pdf]
58. SGJ Senarathne, C Drovandi and J McGree (2020) A Laplace-based algorithm for Bayesian adaptive design. Statistics and Computing. 30, 1183-1208 [​link, pdf]
57. AA Alahmadi, JA Flegg, DG Cochrane, C Drovandi and JM Keith (2020) A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models.  Royal Society Open Science. 7, 191315 [pdf]
56. DN Borg, IB Stewart, JO Osborne, C Drovandi, JT Costello, J Stanley, GM Minett. (2020) The Effects of Daily Cold-Water Recovery and Postexercise Hot-Water Immersion on Training-Load Tolerance During 5 Days of Heat-Based Training.  International Journal of Sports Physiology and Performance​. 15, 639-647 [link, pdf]
55. Z An, DJ Nott and C Drovandi (2020) Robust Bayesian Synthetic Likelihood via a Semi-Parametric Approach. Statistics and Computing. 30, 543-557 [link, epdf, pdf]
54. SGJ Senarathne, C Drovandi and J McGree (2020) Bayesian sequential design for Copula models. TEST. 29, 454-478 [pdf, link]
53. MA MacNeil, C Mellin, S Matthews, NH Wolff, TR McClanahan, M Devlin, C Drovandi, K Mengersen & NAJ Graham  (2019) Water quality mediates resilience on the Great Barrier Reef.  Nature Ecology and Evolution.  3, 620–627​ [pdf, link]
52. BN Vo., CC Drovandi and AN Pettitt (2019) Bayesian parametric bootstrap for models with intractable likelihoods.  Bayesian Analysis.  14, 211-234​ [pdf]
51. Z An, LF South, DJ Nott and CC Drovandi (2019) Accelerating Bayesian synthetic likelihood with the graphical lasso. Journal of Computational and Graphical Statistics. 28, 471-475 [link]
50. LF South, AN Pettitt, and CC Drovandi (2019) Sequential Monte Carlo for static Bayesian models with independent Markov chain Monte Carlo proposals. Bayesian Analysis. 14, 773-796 [pdf]
​49. VMH Ong, DJ Nott, M-N Tran, SA Sisson, and CC Drovandi (2018) Likelihood-free inference in high dimensions with synthetic likelihood.  Computational Statistics & Data Analysis.  128, 271-291. [link]​
48. BAJ Lawson, K Burrage, P Burrage, CC Drovandi and A Bueno-Orovio (2018). Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation. Frontiers in Physiology, 9, 1114. [pdf]
47. Borg DN, Stewart IB, Costello JT, Drovandi CC, and Minett GM. (2018). The impact of environmental temperature deception on perceived exertion during fixed-intensity exercise in the heat in trained-cyclists. Physiology and Behavior, 194, 333-340. [pdf, link]
46. JF Zhang, NE Papanikolaou, T Kypraios and CC Drovandi (2018) Optimal experimental design for predator–prey functional response experiments. Journal of the Royal Society Interface, 15: 20180186. [link]
45. MI Cespedes, J McGree, CC Drovandi, K Mengersen, JD Doecke, and J Fripp (2018) An efficient algorithm for estimating brain covariance networks. PLoS ONE, 13(7): e0198583. [pdf]
44. XJ Lee, M Hainy, JP McKeone, CC Drovandi, AN Pettitt (2018) ABC model selection for spatial extremes models applied to South Australian maximum temperature data. Computational Statistics and Data Analysis, 128: 128-144. [pdf, link]
43. BXW. Liew, CC Drovandi, S Clifford, JWL Keogh, S Morris and K Netto (2018) Joint-level energetics differentiate isoinertial from speed-power resistance training—a Bayesian analysis. PeerJ,  6:e4620. [pdf]
42. W Xueou, DJ Nott, CC Drovandi, K Mengersen and M Evans (2018). Using history matching for prior choice.  Technometrics, 60, 445-460.  [pdf, link]
41. BAJ Lawson, CC Drovandi, N Cusimano, P Burrage, B Rodriguez & K Burrage (2018) Unlocking datasets by calibrating populations of models to data density: a study in atrial electrophysiology. Science Advances. 4(1), e1701676 [pdf] 
40. CC Drovandi, MT Moores, and RJ Boys. (2018) Accelerating pseudo-marginal MCMC using Gaussian processes. Computational Statistics & Data Analysis, 118: 1-17. 
39. Ong, Victor M. H., Nott, David J., Tran, Minh-Ngoc, Sisson, Scott A., & Drovandi, Christopher C. (2018) Variational Bayes with synthetic likelihood. Statistics and Computing. 28(4): 971–988.
38. M.B. Dehideniya, C.C. Drovandi and J. McGree (2018) Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology.  Computational Statistics and Data Analysis. 124: 277-297 [link]
37. Overstall, Antony M., McGree, James, & Drovandi, Christopher C. (2018) An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions. Statistics and Computing. 28(2):  343–358. [pdf]
36. Price, Leah F., Drovandi, Christopher C., Lee, Anthony, & Nott, David J. (2018) Bayesian synthetic likelihood. Journal of Computational and Graphical Statistics. 27(1): 1-11. [pdf, link, code]
35. Nott, David J., Drovandi, Christopher C., Mengersen, Kerrie, & Evans, Michael (2018) Approximation of Bayesian predictive p-values with regression ABC. Bayesian Analysis. 13(1), pp. 59-83. [pdf]
34. Drovandi, Christopher C. & Tran, Minh-Ngoc (2018) Improving the efficiency of fully Bayesian optimal design of experiments using randomised quasi-Monte Carlo. Bayesian Analysis, 13(1), pp. 139-162. [pdf]
33. Drovandi, Christopher C., Holmes, Christopher, McGree, James M., Mengersen, Kerrie, Richardson, Sylvia, & Ryan, Elizabeth G. (2017) Principles of experimental design for Big Data analysis. Statistical Science, 32(3), pp. 385-404. [pdf, link]
32. Chen, Carla Chia-Ming, Drovandi, Christopher C., Keith, Jonathan M., Anthony, Ken, Caley, M. Julian, & Mengersen, Kerrie (2017) Bayesian semi-individual agent based model with approximate Bayesian computation for parameters calibration: Modelling Crown-of-Thorns Starfish population on the Great Barrier Reef. Ecological Modelling, 364, pp. 113-123. [pdf, link]
31. Chen, Carla Chia-Ming, Bourne, David G., Drovandi, Christopher C., Mengersen, Kerrie, Willis, Bette L., Caley, M. Julian & Sato, Yui (2017) Modelling environmental drivers of black band disease outbreaks in populations of foliose corals in the genus Montipora. PeerJ, 5, e3438.
30. Cespedes, Marcela I., Fripp, Jurgen, McGree, James M., Drovandi, Christopher C., Mengersen, Kerrie, & Doecke, James D. (2017) Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference. BMJ Open, 7, Article number-e012174.
29. Ryan, Elizabeth G., Drovandi, Christopher C., McGree, James M., & Pettitt, Anthony N. (2016) A review of modern computational algorithms for Bayesian optimal design. International Statistical Review, 84(1), pp. 128-154. 
28. McGree, James, Drovandi, Christopher C., White, Gentry, & Pettitt, Anthony N. (2016) A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design. Statistics and Computing, 26(5), pp. 1121-1136.
27. Ryan, Caitriona M., Drovandi, Christopher C., & Pettitt, Anthony N. (2016) Optimal Bayesian experimental design for models with intractable likelihoods using indirect inference applied to biological process models. Bayesian Analysis, 11(3), pp. 857-883. (pdf)
26. Drovandi, Christopher C., Pettitt, Anthony N., & McCutchan, Roy A. (2016) Exact and approximate Bayesian inference for low count time series models with intractable likelihoods. Bayesian Analysis, 11(2), pp. 325-352. (pdf)
25. Kang, Su Yun, McGree, James M., Drovandi, Christopher C., Caley, M. Julian, & Mengersen, Kerrie L. (2016) Bayesian adaptive design: Improving the effectiveness of monitoring of the Great Barrier Reef. Ecological Applications, 26(8), pp. 2637-2648. 
24. Drovandi, Christopher C. & McCutchan, Roy A. (2016) Alive SMC^2: Bayesian model selection for low-count time series models with intractable likelihoods. Biometrics, 72(2), pp. 344-353.  (pdf)
23. Mengersen, Kerrie, Drovandi, Christopher C., Robert, Christian P., Pyne, David B., & Gore, Christopher G. (2016) Bayesian estimation of small effects in exercise and sports science. PLoS ONE, 11(4), Article Number-e0147311.
22. Trost, Stewart G., Drovandi, Christopher C., & Pfeiffer, Karin (2016) Developmental trends in the energy cost of physical activities performed by youth. Journal of Physical Activity and Health, 13(6), S35-S40. 
21. Drovandi, Christopher C., Cusimano, Nicole, Psaltis, Steven, Lawson, Brodie A. J., Pettitt, Anthony N., Burrage, Pamela, et al. (2016) Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments. Journal of the Royal Society Interface, 13(121), Article no. 20160214. 
20. Vo, Brenda N., Drovandi, Christopher C., Pettitt, Anthony N., & Pettet, Graeme J. (2015) Melanoma cell colony expansion parameters revealed by approximate Bayesian computation. PLOS Computational Biology, 11(12), e1004635. ​
19. Vo, Brenda N., Drovandi, Christopher C., Pettitt, Anthony N., & Simpson, Matthew J. (2015) Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation. Mathematical Biosciences, 263, pp. 133-142. 
18. Drovandi, Christopher C., Pettitt, Anthony N., & Lee, Anthony (2015) Bayesian indirect inference using a parametric auxiliary model. Statistical Science, 30(1), pp. 72-95.
17. Ryan, Elizabeth G., Drovandi, Christopher C., & Pettitt, Anthony N. (2015) Fully Bayesian experimental design for pharmacokinetic studies. Entropy, 17(3), pp. 1063-1089. 
16. Lee, Xing Ju, Drovandi, Christopher C., & Pettitt, Anthony N. (2015) Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets. Biometrics, 71(1), pp. 198-207. 
15. Ali, Hammad, Cameron, Ewan, Drovandi, Christopher C., McCaw, James M., Guy, Rebecca J., Middleton, Melanie, et al. (2015) A new approach to estimating trends in chlamydia incidence. Sexually Transmitted Infections, 91, pp. 513-519.
14. Moores, Matthew T., Drovandi, Christopher C., Mengersen, Kerrie, & Robert, Christian P. (2015) Pre-processing for approximate Bayesian computation in image analysis. ​Statistics and Computing, 25(1), pp. 23-33. 
13. Ryan, Elizabeth, Drovandi, Christopher C., & Pettitt, Anthony N. (2015) Simulation-based fully Bayesian experimental design for mixed effects models. Computational Statistics and Data Analysis, 92, pp. 26-39. 
12. Ryan, Elizabeth, Drovandi, Christopher C., Thompson, Helen, & Pettitt, Anthony N. (2014) Towards Bayesian experimental design for nonlinear models that require a large number of sampling times. Computational Statistics and Data Analysis, 70, pp. 45-60. ​
11. Drovandi, Christopher C., Pettitt, Anthony N., Henderson, Robert D., & McCombe, Pamela A. (2014) Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation. Computational Statistics & Data Analysis, 72, pp. 128-146. ​ 
10. Drovandi, Christopher C., McGree, James, & Pettitt, Anthony N. (2014) A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design. Journal of Computational and Graphical Statistics, 23(1), pp. 3-24.  ​ 
9. Drovandi, Christopher C. & Pettitt, Anthony N. (2013) Bayesian experimental design for models with intractable likelihoods. Biometrics, 69(4), pp. 937-948. ​ ​ 
8. Drovandi, Christopher C., McGree, James, & Pettitt, Anthony N. (2013) Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data. Computational Statistics and Data Analysis, 57(1). ​ ​ 
7. McGree, James M., Drovandi, Christopher C., Thompson, Helen, Eccleston, John, Duffull, Stephen, Mengersen, Kerrie, et al. (2012) Adaptive Bayesian compound designs for dose finding studies. Journal of Statistical Planning and Inference, 142(6), pp. 1480-1492. ​
6. McGree, J.M., Drovandi, C.C., & Pettitt, A.N. (2012) A sequential Monte Carlo approach to design for population pharmacokinetics studies. Journal of Pharmacokinetics and Pharmacodynamics, 39(5), pp. 519-526. ​
5. Drovandi, Christopher C. & Pettitt, Anthony N. (2011) Estimation of parameters for macroparasite population evolution using approximate Bayesian computation. Biometrics, 67(1), pp. 225-233. ​
4. Drovandi, Christopher C., Pettitt, Anthony N., & Faddy, Malcolm J. (2011) Approximate Bayesian computation using indirect inference. Journal of the Royal Statistical Society, Series C (Applied Statistics), 60(3), pp. 317-337. ​
3. Drovandi, Christopher C. & Pettitt, Tony (2011) Likelihood-free Bayesian estimation of multivariate quantile distributions. Computational Statistics and Data Analysis, 55(9), pp. 2541-2556.​
2. Drovandi, Christopher C. & Pettitt, Anthony N. (2011) Using approximate Bayesian computation to estimate transmission rates of nosocomial pathogens. Statistical Communications in Infectious Diseases, 3(1). ​
1. Drovandi, Christopher C. & Pettitt, Anthony N. (2008) Multivariate Markov Process Models for the transmission of methicillin-resistant Staphylococcus Aureus in a hospital ward. Biometrics, 64(3), pp. 851-859. ​