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February 2018 Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models
Trevelyan J. McKinley, Ian Vernon, Ioannis Andrianakis, Nicky McCreesh, Jeremy E. Oakley, Rebecca N. Nsubuga, Michael Goldstein, Richard G. White
Statist. Sci. 33(1): 4-18 (February 2018). DOI: 10.1214/17-STS618

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.

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Trevelyan J. McKinley. Ian Vernon. Ioannis Andrianakis. Nicky McCreesh. Jeremy E. Oakley. Rebecca N. Nsubuga. Michael Goldstein. Richard G. White. "Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models." Statist. Sci. 33 (1) 4 - 18, February 2018. https://doi.org/10.1214/17-STS618

Information

Published: February 2018
First available in Project Euclid: 2 February 2018

zbMATH: 07031386
MathSciNet: MR3757500
Digital Object Identifier: 10.1214/17-STS618

Keywords: Approximate Bayesian Computation , Bayesian inference , emulation , history matching , infectious disease models

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.33 • No. 1 • February 2018
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