Search Results - "Drovandi, C."

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  1. 1

    Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation by Drovandi, C. C., Pettitt, A. N.

    Published in Biometrics (01-03-2011)
    “…We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The…”
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  2. 2

    A Review of Modern Computational Algorithms for Bayesian Optimal Design by Ryan, Elizabeth G., Drovandi, Christopher C., McGree, James M., Pettitt, Anthony N.

    Published in International statistical review (01-04-2016)
    “…Bayesian experimental design is a fast growing area of research with many real-world applications. As computational power has increased over the years, so has…”
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  3. 3

    Bayesian detectability of induced polarization in airborne electromagnetic data by Davies, L, Ley-Cooper, A Y, Sutton, M, Drovandi, C

    Published in Geophysical journal international (01-12-2023)
    “…SUMMARY Detection of induced polarization (IP) effects in airborne electromagnetic measurements does not yet have an established methodology. This work…”
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  4. 4

    Bayesian Indirect Inference Using a Parametric Auxiliary Model by Drovandi, Christopher C., Pettitt, Anthony N., Lee, Anthony

    Published in Statistical science (01-02-2015)
    “…Indirect inference (II) is a methodology for estimating the parameters of an intractable (generative) model on the basis of an alternative parametric…”
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  5. 5

    Bayesian Estimation of Small Effects in Exercise and Sports Science by Mengersen, Kerrie L, Drovandi, Christopher C, Robert, Christian P, Pyne, David B, Gore, Christopher J

    Published in PloS one (13-04-2016)
    “…The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in…”
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  6. 6

    LooplessFluxSampler: an efficient toolbox for sampling the loopless flux solution space of metabolic models by Saa, Pedro A, Zapararte, Sebastian, Drovandi, Christopher C, Nielsen, Lars K

    Published in BMC bioinformatics (02-01-2024)
    “…Uniform random sampling of mass-balanced flux solutions offers an unbiased appraisal of the capabilities of metabolic networks. Unfortunately, it is impossible…”
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  7. 7

    A Laplace-based algorithm for Bayesian adaptive design by Senarathne, S. G. J., Drovandi, C. C., McGree, J. M.

    Published in Statistics and computing (01-09-2020)
    “…This article presents a novel Laplace-based algorithm that can be used to find Bayesian adaptive designs under model and parameter uncertainty. Our algorithm…”
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  8. 8
  9. 9

    The impact of environmental temperature deception on perceived exertion during fixed-intensity exercise in the heat in trained-cyclists by Borg, D.N., Stewart, I.B., Costello, J.T., Drovandi, C.C., Minett, G.M.

    Published in Physiology & behavior (01-10-2018)
    “…This study examined the effect of environmental temperature deception on the rating of perceived exertion (RPE) during 30 min of fixed-intensity cycling in the…”
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  10. 10

    An efficient algorithm for estimating brain covariance networks by Cespedes, Marcela I, McGree, James, Drovandi, Christopher C, Mengersen, Kerrie, Doecke, James D, Fripp, Jurgen

    Published in PloS one (12-07-2018)
    “…Often derived from partial correlations or many pairwise analyses, covariance networks represent the inter-relationships among regions and can reveal important…”
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  11. 11

    Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation by Lawson, Brodie A, Burrage, Kevin, Burrage, Pamela, Drovandi, Christopher C, Bueno-Orovio, Alfonso

    Published in Frontiers in physiology (28-08-2018)
    “…Rotor stability and meandering are key mechanisms determining and sustaining cardiac fibrillation, with important implications for anti-arrhythmic drug…”
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  12. 12

    Fully Bayesian Experimental Design for Pharmacokinetic Studies by Elizabeth G. Ryan, Christopher C. Drovandi, Anthony N. Pettitt

    Published in Entropy (Basel, Switzerland) (01-03-2015)
    “…Utility functions in Bayesian experimental design are usually based on the posterior distribution. When the posterior is found by simulation, it must be…”
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  13. 13

    Bayesian sequential design for Copula models by Senarathne, S. G. J., Drovandi, C. C., McGree, J. M.

    Published in Test (Madrid, Spain) (01-06-2020)
    “…Bayesian design requires determining the value of controllable variables in an experiment to maximise the information that will be obtained for subsequently…”
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  14. 14

    Melanoma Cell Colony Expansion Parameters Revealed by Approximate Bayesian Computation by Vo, Brenda N, Drovandi, Christopher C, Pettitt, Anthony N, Pettet, Graeme J

    Published in PLoS computational biology (01-12-2015)
    “…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…”
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  15. 15

    A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models by Alahmadi, Amani A, Flegg, Jennifer A, Cochrane, Davis G, Drovandi, Christopher C, Keith, Jonathan M

    Published in Royal Society open science (01-03-2020)
    “…The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential…”
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  16. 16

    Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments by Drovandi, C. C., Cusimano, N., Psaltis, S., Lawson, B. A. J., Pettitt, A. N., Burrage, P., Burrage, K.

    Published in Journal of the Royal Society interface (01-08-2016)
    “…Between-subject and within-subject variability is ubiquitous in biology and physiology, and understanding and dealing with this is one of the biggest…”
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  17. 17

    A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design by McGree, J. M., Drovandi, C. C., White, G., Pettitt, A. N.

    Published in Statistics and computing (01-09-2016)
    “…Motivated by the need to sequentially design experiments for the collection of data in batches or blocks, a new pseudo-marginal sequential Monte Carlo…”
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  18. 18

    Joint-level energetics differentiate isoinertial from speed-power resistance training-a Bayesian analysis by Liew, Bernard X W, Drovandi, Christopher C, Clifford, Samuel, Keogh, Justin W L, Morris, Susan, Netto, Kevin

    Published in PeerJ (San Francisco, CA) (12-04-2018)
    “…There is convincing evidence for the benefits of resistance training on vertical jump improvements, but little evidence to guide optimal training prescription…”
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  19. 19

    Modelling environmental drivers of black band disease outbreaks in populations of foliose corals in the genus Montipora by Chen, Carla C M, Bourne, David G, Drovandi, Christopher C, Mengersen, Kerrie, Willis, Bette L, Caley, M Julian, Sato, Yui

    Published in PeerJ (San Francisco, CA) (12-06-2017)
    “…Seawater temperature anomalies associated with warming climate have been linked to increases in coral disease outbreaks that have contributed to coral reef…”
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  20. 20

    A sequential Monte Carlo approach to derive sampling times and windows for population pharmacokinetic studies by McGree, J. M., Drovandi, C. C., Pettitt, A. N.

    “…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…”
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