Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers.
Akira Endo 
;
Edwin van Leeuwen ;
Marc Baguelin ;
(2019)
Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers.
Epidemics, 29.
100363-.
ISSN 1755-4365
DOI: 10.1016/j.epidem.2019.100363
;
Edwin van Leeuwen ;
Marc Baguelin ;
(2019)
Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers.
Epidemics, 29.
100363-.
ISSN 1755-4365
DOI: 10.1016/j.epidem.2019.100363
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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