T.J. McKinley
Approximate Bayesian Computation and simulation-based inference for complex stochastic epidemic models
McKinley, T.J.; Vernon, I.; Andrianakis, I.; McCreesh, N.; Oakley, J.E.; Nsubuga, R.; Goldstein, M.; White, R.G.
Authors
Professor Ian Vernon i.r.vernon@durham.ac.uk
Professor
I. Andrianakis
N. McCreesh
J.E. Oakley
R. Nsubuga
M. Goldstein
R.G. White
Abstract
Approximate Bayesian Computation (ABC) and other simulation-based inference methods are becoming increasingly used for inference in complex systems, due to their relative ease-of-implementation. We briefly review some of the more popular variants of ABC and their application in epidemiology, before using a real-world model of HIV transmission to illustrate some of challenges when applying ABC methods to high-dimensional, computationally intensive models. We then discuss an alternative approach—history matching—that aims to address some of these issues, and conclude with a comparison between these different methodologies.
Citation
McKinley, T., Vernon, I., Andrianakis, I., McCreesh, N., Oakley, J., Nsubuga, R., …White, R. (2018). Approximate Bayesian Computation and simulation-based inference for complex stochastic epidemic models. Statistical Science, 33(1), 4-18. https://doi.org/10.1214/17-sts618
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 10, 2017 |
Online Publication Date | Feb 2, 2018 |
Publication Date | Feb 2, 2018 |
Deposit Date | Sep 26, 2016 |
Publicly Available Date | Mar 29, 2024 |
Journal | Statistical Science |
Print ISSN | 0883-4237 |
Electronic ISSN | 2168-8745 |
Publisher | Institute of Mathematical Statistics |
Peer Reviewed | Peer Reviewed |
Volume | 33 |
Issue | 1 |
Pages | 4-18 |
DOI | https://doi.org/10.1214/17-sts618 |
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Copyright Statement
This work is licensed under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/
Published Journal Article
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
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