Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers
Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers
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DOI：
10.1016/j.epidem.2019.100363
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发表时间：
2019-12-01
期刊：
影响因子：
3.8
通讯作者：
Baguelin, Marc
中科院分区：
文献类型：
--
作者：
Endo, Akira;van Leeuwen, Edwin;Baguelin, Marc
粒子马尔可夫链蒙特卡罗（PMCMC）方法是利用时间序列数据高效探索高维参数空间的有力工具。我们以最少但足够的理论背景说明PMCMC的整体情况，以支持生物医学/健康科学领域的读者将PMCMC应用于他们的研究。用R代码给出了PMCMC在传染病动力学模型中的应用实例。
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.