A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation
A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation
复制标题
DOI：
10.1016/j.mbs.2016.07.001
复制
发表时间：
2017-05-01
影响因子：
4.3
通讯作者：
Prangle, Dennis
中科院分区：
文献类型：
--
作者：
Kypraios, Theodore;Neal, Peter;Prangle, Dennis
基于似然的疾病暴发数据推断可能非常具有挑战性，因为数据本身具有依赖性，而且它们通常是不完整的。在本文中，我们回顾了最近的近似贝叶斯计算（ABC）方法，这些方法通过拟合随机流行病模型而不必计算观测数据的似然来分析这些数据。我们考虑了非时间数据和时间数据，并用一些具有不同模型和数据集的示例说明了这些方法。此外，我们还提出了对现有算法的扩展，这些算法易于实现，并对现有方法进行了改进。最后，实现本文中提出的算法的r代码可在https://github.comficypraiosiepiABC上获得。(C) 2016 Elsevier Inc.版权所有。
Likelihood-based inference for disease outbreak data can be very challenging due to the inherent dependence of the data and the fact that they are usually incomplete. In this paper we review recent Approximate Bayesian Computation (ABC) methods for the analysis of such data by fitting to them stochastic epidemic models without having to calculate the likelihood of the observed data. We consider both non temporal and temporal-data and illustrate the methods with a number of examples featuring different models and datasets. In addition, we present extensions to existing algorithms which are easy to implement and provide an improvement to the existing methodology. Finally, R. code to implement the algorithms presented in the paper is available on https://github.comficypraiosiepiABC. (C) 2016 Elsevier Inc. All rights reserved.