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- C C Drovandi, A N Pettitt
- Biometrics
- 2011

We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling… (More)

- Christopher C. Drovandi, Anthony N. Pettitt
- Computational Statistics & Data Analysis
- 2011

- Christopher C. Drovandi, James M. McGree, Anthony N. Pettitt
- Computational Statistics & Data Analysis
- 2013

This the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was… (More)

- Brenda N Vo, Christopher C Drovandi, Anthony N Pettitt, Matthew J Simpson
- Mathematical biosciences
- 2015

Wound healing and tumour growth involve collective cell spreading, which is driven by individual motility and proliferation events within a population of cells. Mathematical models are often used to interpret experimental data and to estimate the parameters so that predictions can be made. Existing methods for parameter estimation typically assume that… (More)

- C C Drovandi, N Cusimano, +4 authors K Burrage
- Journal of the Royal Society, Interface
- 2016

Between-subject and within-subject variability is ubiquitous in biology and physiology, and understanding and dealing with this is one of the biggest challenges in medicine. At the same time, it is difficult to investigate this variability by experiments alone. A recent modelling and simulation approach, known as population of models (POM), allows this… (More)

- J. M. McGree, C. C. Drovandi, A. N. Pettitt
- Journal of Pharmacokinetics and Pharmacodynamics
- 2012

Here we present a sequential Monte Carlo approach that can be used to find optimal designs. Our focus is on the design of population pharmacokinetic studies where the derivation of sampling windows is required, along with the optimal sampling schedule. The search is conducted via a particle filter which traverses a sequence of target distributions… (More)

- Christopher C Drovandi, Anthony N Pettitt
- Biometrics
- 2013

In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable likelihoods. The approach combines a commonly used methodology for robust experimental design, based on Markov chain Monte Carlo sampling, with approximate Bayesian computation (ABC) to ensure that no… (More)

- Brenda N. Vo, Christopher C. Drovandi, Anthony N. Pettitt, Graeme J. Pettet
- PLoS Computational Biology
- 2015

In vitro studies and mathematical models are now being widely used to study the underlying mechanisms driving the expansion of cell colonies. This can improve our understanding of cancer formation and progression. Although much progress has been made in terms of developing and analysing mathematical models, far less progress has been made in terms of… (More)

- C C Drovandi, A N Pettitt
- Biometrics
- 2008

Methicillin-resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245,… (More)

- Elizabeth G. Ryan, Christopher C. Drovandi, M. Helen Thompson, Anthony N. Pettitt
- Computational Statistics & Data Analysis
- 2014

The use of Bayesian methodologies for solving optimal experimental design problems has increased. Many of these methods have been found to be computationally intensive for design problems that require a large number of design points. A simulation-based approach that can be used to solve optimal design problems in which one is interested in finding a large… (More)