Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers

被引：44
|
作者：
Endo, Akira [1 ]
van Leeuwen, Edwin [2 ]
Baguelin, Marc [1 ,2 ,3 ]
机构：
[1] London Sch Hyg & Trop Med, London, England
[2] Publ Hlth England, London, England
[3] Imperial Coll, London, England
来源：
EPIDEMICS | 2019年 / 29卷
关键词：
Particle Markov-chain Monte Carlo; State-space models; Hidden Markov process; Particle filter; Sequential Monte Carlo; BAYESIAN-INFERENCE;
D O I：
10.1016/j.epidem.2019.100363
中图分类号：
R51 [传染病];
学科分类号：
100401 ;
摘要：
The particle Markov-chain Monte Carlo (PMCMC) method is a powerful tool to efficiently explore high-dimensional parameter space using time-series data. We illustrate an overall picture of PMCMC with minimal but sufficient theoretical background to support the readers in the field of biomedical/health science to apply PMCMC to their studies. Some working examples of PMCMC applied to infectious disease dynamic models are presented with R code.
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页数：13
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