Collapsing of non-centered parameterised MCMC algorithms with applications to epidemic models

Neal, Peter John and Xiang, Fei (2017) Collapsing of non-centered parameterised MCMC algorithms with applications to epidemic models. Scandinavian Journal of Statistics, 44 (1). pp. 81-96. ISSN 0303-6898

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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.

Item Type:
Journal Article
Journal or Publication Title:
Scandinavian Journal of Statistics
Additional Information:
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 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Uncontrolled Keywords:
?? collapsingmeaslesnon-centered mcmc algorithmsspatial epidemicsstochastic epidemic modelsstatistics and probabilitystatistics, probability and uncertainty ??
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Deposited On:
19 May 2016 07:56
Last Modified:
31 Dec 2023 00:41