52. Z An, LF South, DJ Nott and CC Drovandi (2018) Accelerating Bayesian synthetic likelihood with the graphical lasso. Journal of Computational and Graphical Statistics. To appear.
51. LF South, AN Pettitt, and CC Drovandi. (2018) Sequential Monte Carlo for static Bayesian models with independent Markov chain Monte Carlo proposals. Bayesian Analysis. To appear. [pdf]
​50. 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]​
49. 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]
48. 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. [link]
47. 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]
46. 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]
45. 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]
44. 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]
43. W Xueou, DJ Nott, CC Drovandi, K Mengersen and M Evans (2018). Using history matching for prior choice.  Technometrics (In Press).  [pdf, link]
42. 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] 
41. CC Drovandi, MT Moores, and RJ Boys. (2018) Accelerating pseudo-marginal MCMC using Gaussian processes. Computational Statistics & Data Analysis, 118: 1-17. 
40. 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.
39. M.B. Dehideniya, C.C. Drovandi and J. McGree (2018) Efficient Bayesian design for discriminating between models with intractable likelihoods in epidemiology.  Computational Statistics and Data Analysis. 124: 277-297 [link]
38. 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]
37. Vo, Brenda N., Drovandi, Christopher C., & Pettitt, Anthony N. (2018) Bayesian parametric bootstrap for models with intractable likelihoods.  Bayesian Analysis.  (In Press) [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. [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. 
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. ​