Collapsing of Non-centred Parameterized MCMC Algorithms with Applications to Epidemic Models
Collapsing of Non-centred Parameterized MCMC Algorithms with Applications to Epidemic Models
复制标题
非中心参数化 MCMC 算法在流行病模型中的崩溃
DOI：
10.1111/sjos.12242
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发表时间：
2016
影响因子：
1
通讯作者：
Neal P
中科院分区：
文献类型：
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作者：
Neal P
许多马尔可夫链蒙特卡罗 (MCMC) 算法的实现都需要数据增强。包含增强数据通常可以导致模型中某些参数的众所周知的概率分布的条件分布。在这种情况下，折叠（整合参数）已被证明可以提高 MCMC 算法的性能。我们展示了如何在流行病模型中整合感染率参数，从而针对两种截然不同的流行病场景（来自多类型 SIR 流行病的最终结果数据和来自空间 SI 流行病的纵向数据）产生有效的 MCMC 算法。由此产生的 MCMC 算法为现实生活中的流行病数据集提供了新的见解。
Data augmentation is required for the implementation of many Markov chain Monte Carlo (MCMC) algorithms. The inclusion of augmented data can often lead to conditional distributions from well‐known probability distributions for some of the parameters in the model. In such cases, collapsing (integrating out parameters) has been shown to improve the performance of MCMC algorithms. We show how integrating out the infection rate parameter in epidemic models leads to efficient MCMC algorithms for two very different epidemic scenarios, final outcome data from a multitype SIR epidemic and longitudinal data from a spatial SI epidemic. The resulting MCMC algorithms give fresh insight into real‐life epidemic data sets.