Open Access
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


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.


Download Citation

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.


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

Vol.33 • No. 1 • February 2018
Back to Top