Journal article

A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models

Amani A Alahmadi, Jennifer A Flegg, Davis G Cochrane, Christopher C Drovandi, Jonathan M Keith

Royal Society Open Science | ROYAL SOC | Published : 2020

Abstract

The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequenti..

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University of Melbourne Researchers

Grants

Awarded by Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers


Funding Acknowledgements

The authors are grateful to the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers for their support of this project no. CE140100049.