McKinley, T. J. and Vernon, I. and Andrianakis, I. and McCreesh, N. and Oakley, J. E. and Nsubuga, R. and Goldstein, M. and White, R. G. (2018) 'Approximate Bayesian Computation and simulation-based inference for complex stochastic epidemic models.', Statistical science., 33 (1). pp. 4-18.
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.
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1214/17-STS618|
|Publisher statement:||This work is licensed under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/|
|Date accepted:||10 June 2017|
|Date deposited:||20 September 2017|
|Date of first online publication:||02 February 2018|
|Date first made open access:||22 February 2018|
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