Efficient MCMC for temporal epidemics via parameter reduction
Xiang, Fei and Neal, Peter
(2014)
Efficient MCMC for temporal epidemics via parameter reduction.
Computational Statistics and Data Analysis, 80.
pp. 240-250.
ISSN 0167-9473
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Abstract
An efficient, generic and simple to use Markov chain Monte Carlo (MCMC) algorithm for partially observed temporal epidemic models is introduced. The algorithm is designed to be adaptive so that it can easily be used by non-experts. There are two key features incorporated in the algorithm to develop an efficient algorithm, parameter reduction and efficient, multiple updates of the augmented infection times. The algorithm is successfully applied to two real life epidemic data sets, the Abakaliki smallpox data and the 2001 UK foot-and-mouth epidemic in Cumbria.
Item Type:
Journal Article
Journal or Publication Title:
Computational Statistics and Data Analysis
Additional Information:
NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics and Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics and Data Analysis [80, 2014] DOI: 10.1016/j.csda.2014.07.002
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1703
Subjects:
?? sir epidemic modelsdata augmentationadaptive mcmcsmallpoxfoot-and-mouth diseasecomputational theory and mathematicscomputational mathematicsapplied mathematicsstatistics and probability ??
Deposited On:
30 Jan 2015 11:31
Last Modified:
10 Nov 2024 01:10