Search Results - "Christopher C. Drovandi"

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

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

“…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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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

“…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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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

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

“…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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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

“…Rotor stability and meandering are key mechanisms determining and sustaining cardiac fibrillation, with important implications for anti-arrhythmic drug…”
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Fully Bayesian Experimental Design for Pharmacokinetic Studies by Elizabeth G. Ryan, Christopher C. Drovandi, Anthony N. Pettitt

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

“…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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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

“…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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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

“…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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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

“…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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Variational Bayes with synthetic likelihood by Ong, Victor M. H., Nott, David J., Tran, Minh-Ngoc, Sisson, Scott A., Drovandi, Christopher C.

“…Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Gaussian summary statistic for the data, informative for…”
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A Sequential Monte Carlo Algorithm to Incorporate Model Uncertainty in Bayesian Sequential Design by Drovandi, Christopher C., McGree, James M., Pettitt, Anthony N.

“…This article presents a sequential Monte Carlo (SMC) algorithm that can be used for any one-at-a-time Bayesian sequential design problem in the presence of…”
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Bayesian Experimental Design for Models with Intractable Likelihoods by Drovandi, Christopher C, Pettitt, Anthony N

“…In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable…”
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An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions by Overstall, Antony M., McGree, James M., Drovandi, Christopher C.

“…The generation of decision-theoretic Bayesian optimal designs is complicated by the significant computational challenge of minimising an analytically…”
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Modularized Bayesian analyses and cutting feedback in likelihood-free inference by Chakraborty, Atlanta, Nott, David J., Drovandi, Christopher C., Frazier, David T., Sisson, Scott A.

“…There has been much recent interest in modifying Bayesian inference for misspecified models so that it is useful for specific purposes. One popular modified…”
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Alive SMC(2) : Bayesian model selection for low-count time series models with intractable likelihoods by Drovandi, Christopher C, McCutchan, Roy A

“…In this article we present a new method for performing Bayesian parameter inference and model choice for low- count time series models with intractable…”
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Accelerating Bayesian Synthetic Likelihood With the Graphical Lasso by An, Ziwen, South, Leah F., Nott, David J., Drovandi, Christopher C.

“…Simulation-based Bayesian inference methods are useful when the statistical model of interest does not possess a computationally tractable likelihood function…”
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Principles of Experimental Design for Big Data Analysis by Drovandi, Christopher C., Holmes, Christopher C., McGree, James M., Mengersen, Kerrie, Richardson, Sylvia, Ryan, Elizabeth G.

“…Big Datasets are endemic, but are often notoriously difficult to analyse because of their size, heterogeneity and quality. The purpose of this paper is to open…”
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Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation by Vo, Brenda N., Drovandi, Christopher C., Pettitt, Anthony N., Simpson, Matthew J.

“…•Quantify uncertainty in estimates of cell diffusivity D and cell proliferation rate λ.•Using leading edge data, we avoid labelling and counting individual…”
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