Publication details for Ian VernonMcKinley, T. J., Vernon, I., Andrianakis, I., McCreesh, N., Oakley, J. E., Nsubuga, R., Goldstein, M. & White, R. G. (2018). Approximate Bayesian Computation and simulation-based inference for complex stochastic epidemic models. Statistical Science 33(1): 4-18.
- Publication type: Journal Article
- ISSN/ISBN: 0883-4237, 2168-8745
- DOI: 10.1214/17-STS618
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
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.