Rights statement: This is the peer reviewed version of the following article:Neal, P., and Xiang, F. (2017) Collapsing of Non-centred Parameterized MCMC Algorithms with Applications to Epidemic Models. Scand J Statist, 44: 81–96. doi: 10.1111/sjos.12242 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/sjos.12242/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Collapsing of non-centered parameterised MCMC algorithms with applications to epidemic models
AU - Neal, Peter John
AU - Xiang, Fei
N1 - This is the peer reviewed version of the following article:Neal, P., and Xiang, F. (2017) Collapsing of Non-centred Parameterized MCMC Algorithms with Applications to Epidemic Models. Scand J Statist, 44: 81–96. doi: 10.1111/sjos.12242 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/sjos.12242/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2017/3
Y1 - 2017/3
N2 - Data augmentation is required for the implementation of many MCMC algorithms. The inclusion of augmented data can often lead to conditional distributions from well-known probability distributions for some of the parameters in the model.In such cases, collapsing (integrating out parameters) has been shown to improve the performance of MCMC algorithms. We show how integrating out the infection rate parameter in epidemic models leads to efficient MCMC algorithms for two very different epidemic scenarios, final outcome data from a multitype SIR epidemic and longitudinal data from a spatial SI epidemic. The resulting MCMC algorithms give fresh insight into real life epidemic data sets.
AB - Data augmentation is required for the implementation of many MCMC algorithms. The inclusion of augmented data can often lead to conditional distributions from well-known probability distributions for some of the parameters in the model.In such cases, collapsing (integrating out parameters) has been shown to improve the performance of MCMC algorithms. We show how integrating out the infection rate parameter in epidemic models leads to efficient MCMC algorithms for two very different epidemic scenarios, final outcome data from a multitype SIR epidemic and longitudinal data from a spatial SI epidemic. The resulting MCMC algorithms give fresh insight into real life epidemic data sets.
KW - Collapsing
KW - measles
KW - non-centered MCMC algorithms
KW - spatial epidemics
KW - Stochastic epidemic models
U2 - 10.1111/sjos.12242
DO - 10.1111/sjos.12242
M3 - Journal article
VL - 44
SP - 81
EP - 96
JO - Scandinavian Journal of Statistics
JF - Scandinavian Journal of Statistics
SN - 0303-6898
IS - 1
ER -