McKinley, T.J., Vernon, I., Andrianakis, I. et al. (5 more authors) (2018) Approximate Bayesian Computation and simulation-based inference for complex stochastic epidemic models. Statistical science : a review journal of the Institute of Mathematical Statistics, 33 (1). pp. 4-18. ISSN 0883-4237
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Institute of Mathematical Statistics. This is an author produced version of a paper subsequently published in Statistical Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Approximate Bayesian Computation; history matching; emulation; Bayesian inference; infectious disease models |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Funding Information: | Funder Grant number MEDICAL RESEARCH COUNCIL MR/J005088/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Jun 2017 10:22 |
Last Modified: | 23 Feb 2018 10:41 |
Published Version: | https://doi.org/10.1214/17-STS618 |
Status: | Published |
Publisher: | Institute of Mathematical Statistics |
Refereed: | Yes |
Identification Number: | 10.1214/17-STS618 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:117737 |