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. 2019 Dec:29:100363.
doi: 10.1016/j.epidem.2019.100363. Epub 2019 Oct 3.

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

Akira Endo  1 , Edwin van Leeuwen  2 , Marc Baguelin  3
Affiliations
Free article

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

Akira Endo et al. Epidemics. 2019 Dec.
Free article

Abstract

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.

Keywords: Hidden Markov process; Particle Markov-chain Monte Carlo; Particle filter; Sequential Monte Carlo; State-space models.

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