# Browse By Person: Drovandi, Christopher

Up a level |

Group by: Item Type | Date

Number of items:

**27**.## Book Chapter

McGree, James, Drovandi, Christopher C., & Pettitt, Anthony N. (2012) Implementing adaptive dose finding studies using sequential Monte Carlo. In Alston, Clair, Mengersen, Kerrie, & Pettitt, Anthony N. (Eds.)

*Case Studies in Bayesian Statistical Modelling and Analysis.*Wiley, United Kingdom, pp. 361-373.Drovandi, Christopher C. & Pettitt, Anthony N. (2012) Likelihood‐free inference for transmission rates of nosocomial pathogens. In

*Case Studies in Bayesian Statistical Modelling and Analysis.*Wiley, United Kingdom, pp. 374-387.## Journal Article

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.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.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. 131

Drovandi, Christopher C. & Pettitt, Anthony N. (2013) Bayesian experimental design for models with intractable likelihoods.

*Biometrics*,*69*(4), pp. 937-948. 3

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). 119

6

5

McGree, James Matthew, 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. 121

5

3

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. 119

2

2

Drovandi, Christopher C. & Pettitt, Anthony N. (2011) Estimation of parameters for macroparasite population evolution using approximate Bayesian computation.

*Biometrics*,*67*(1), pp. 225-233. 27

26

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. 128

11

7

Drovandi, Christopher C. & Pettitt, Tony (2011) Likelihood-free Bayesian estimation of multivariate quantile distributions.

*Computational Statistics and Data Analysis*,*55*(9), pp. 2541-2556. 5

4

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). 90

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. 4

4

## Conference Paper

Drovandi, Christopher C., McGree, James, & Pettitt, Anthony N. (2014) A sequential Monte Carlo framework for adaptive Bayesian model discrimination designs using mutual information. In Lanzarone, Ettore & Ieva, Francesca (Eds.)

*Springer Proceedings in Mathematics & Statistics : the Contribution of Young Researchers to Bayesian Statistics*, Springer, Milan, Italy, pp. 19-22.Pettitt, Anthony N., Drovandi, Christopher C., & Faddy, Malcolm (2010) Approximate Bayesian computation using auxiliary model based estimates. In Bowman, Adrian (Ed.)

*Proceedings of the 25th International Workshop on Statistical Modelling*, University of Glasgow, Glasgow, pp. 433-438. 23

Whiten, Bill, McDonald, Barry, & Drovandi, Christopher C. (2010) Taxonomic analysis of marine phytoplankton. In Shephard, John, Stacey, Andrew, & Roberts, Anthony (Eds.)

*Proceedings of the 2010 Mathematics and Statistics in Industry Study Group, MISG-2010*, Australian Mathematical Society, RMIT University, Melbourne, VIC, M119-M146.## QUT Thesis

Drovandi, Christopher Colin (2012)

*Bayesian algorithms with applications.*PhD thesis, Queensland University of Technology. 177

## Working Paper

Ryan, Elizabeth G., Drovandi, Christopher C., McGree, James M., & Pettitt, Anthony N. (2014)

*Fully Bayesian optimal experimental design : a review.*[Working Paper] (Unpublished) 24

Ryan, Caitriona, Drovandi, Christopher C., & Pettitt, Anthony (2014)

*Bayesian experimental design for models with intractable likelihoods using indirect inference.*[Working Paper] (Unpublished) 25

Drovandi, Christopher C. (2014)

*Pseudo-marginal algorithms with multiple CPUs.*[Working Paper] (Unpublished) 74

Ryan, Elizabeth, Drovandi, Christopher C., & Pettitt, Anthony N. (2014)

*Simulation-based fully Bayesian experimental design for mixed effects models.*[Working Paper] (Unpublished) 10

Drovandi, Christopher C., Pettitt, Anthony N., & Lee, Anthony (2013)

*Bayesian indirect inference using a parametric auxiliary model.*[Working Paper] (Unpublished) 328

Ryan, Elizabeth G., Drovandi, Christopher C., & Pettitt, Anthony N. (2013)

*Fully Bayesian experimental design for pharmacokinetic studies.*[Working Paper] (Unpublished) 9

McGree, James, Drovandi, Christopher C., & Pettitt, Anthony N. (2012)

*A sequential Monte Carlo approach to the sequential design for discriminating between rival continuous data models.*[Working Paper] (Unpublished) 68

## Other

Drovandi, Christopher C., Pettitt, Anthony N., & McCutchan, Roy A. (2013)

*Exact and approximate Bayesian inference for low count time series models with intractable likelihoods.*(Unpublished) 331

Drovandi, Christopher C. & Pettitt, Anthony N. (2012)

*Discussion of : constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation.*Journal of the Royal Statistical Society, Series B : Statistical Methodology. 23

40

29