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

作者：McKinley Trevelyan J; Vernon Ian; Andrianakis Ioannis; McCreesh Nicky; Oakley Jeremy E; Nsubuga Rebecca N; Goldstein Michael; White Richard G

来源：Statistical Science, 2018, 33(1): 4-18.

DOI：10.1214/17-sts618

• Approximate Bayesian Computation
• history matching
• emulation
• Bayesian inference
• infectious disease models

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.

• 出版日期2018-2