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    Rights statement: 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

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Efficient MCMC for temporal epidemics via parameter reduction

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Efficient MCMC for temporal epidemics via parameter reduction. / Xiang, Fei; Neal, Peter.
In: Computational Statistics and Data Analysis, Vol. 80, 12.2014, p. 240-250.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Xiang, F & Neal, P 2014, 'Efficient MCMC for temporal epidemics via parameter reduction', Computational Statistics and Data Analysis, vol. 80, pp. 240-250. https://doi.org/10.1016/j.csda.2014.07.002

APA

Xiang, F., & Neal, P. (2014). Efficient MCMC for temporal epidemics via parameter reduction. Computational Statistics and Data Analysis, 80, 240-250. https://doi.org/10.1016/j.csda.2014.07.002

Vancouver

Xiang F, Neal P. Efficient MCMC for temporal epidemics via parameter reduction. Computational Statistics and Data Analysis. 2014 Dec;80:240-250. Epub 2014 Jul 14. doi: 10.1016/j.csda.2014.07.002

Author

Xiang, Fei ; Neal, Peter. / Efficient MCMC for temporal epidemics via parameter reduction. In: Computational Statistics and Data Analysis. 2014 ; Vol. 80. pp. 240-250.

Bibtex

@article{c7140b9f493e4d49961bd1e0251ca596,
title = "Efficient MCMC for temporal epidemics via parameter reduction",
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.",
keywords = "SIR epidemic models, Data augmentation, Adaptive MCMC, Smallpox, Foot-and-mouth disease",
author = "Fei Xiang and Peter Neal",
note = "NOTICE: this is the author{\textquoteright}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",
year = "2014",
month = dec,
doi = "10.1016/j.csda.2014.07.002",
language = "English",
volume = "80",
pages = "240--250",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Efficient MCMC for temporal epidemics via parameter reduction

AU - Xiang, Fei

AU - Neal, Peter

N1 - 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

PY - 2014/12

Y1 - 2014/12

N2 - 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.

AB - 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.

KW - SIR epidemic models

KW - Data augmentation

KW - Adaptive MCMC

KW - Smallpox

KW - Foot-and-mouth disease

U2 - 10.1016/j.csda.2014.07.002

DO - 10.1016/j.csda.2014.07.002

M3 - Journal article

VL - 80

SP - 240

EP - 250

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

ER -