Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models
Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models
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DOI：
10.1214/17-sts618
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发表时间：
2018-02-01
影响因子：
5.7
通讯作者：
White, Richard G.
中科院分区：
文献类型：
--
作者：
McKinley, Trevelyan J.;Vernon, Ian;White, Richard G.
近似贝叶斯计算（ABC）和其他基于仿真的推理方法由于相对容易实现，越来越多地用于复杂系统中的推理。我们简要回顾了一些比较流行的ABC变体及其在流行病学中的应用，然后使用真实世界的HIV传播模型来说明将ABC方法应用于高维计算密集型模型时的一些挑战。然后，我们讨论另一种方法——历史匹配——旨在解决其中的一些问题，并以这些不同方法之间的比较作为结论。
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.