Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models

• Autores: Trevelyan J. McKinley, Ian Vernon, Ioannis Andrianakis, Nicky McCreesh, Jeremy E. Oakley, Rebecca N. Nsubuga, Michael Goldstein, Richard G. White
• Localización: Statistical science, ISSN 0883-4237, Vol. 33, Nº. 1, 2018, págs. 4-18
• Idioma: inglés
• DOI: 10.1214/17-sts618
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• Resumen

  • 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.