A case study in non-centering for data augmentation:stochastic epidemics

Neal, Peter John and Roberts, Gareth (2005) A case study in non-centering for data augmentation:stochastic epidemics. Statistics and Computing, 15 (4). pp. 315-327. ISSN 0960-3174

Full text not available from this repository.


In this paper, we introduce non-centered and partially non-centered MCMC algorithms for stochastic epidemic models. Centered algorithms previously considered in the literature perform adequately well for small data sets. However, due to the high dependence inherent in the models between the missing data and the parameters, the performance of the centered algorithms gets appreciably worse when larger data sets are considered. Therefore non-centered and partially non-centered algorithms are introduced and are shown to out perform the existing centered algorithms.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
30 Nov 2015 09:04
Last Modified:
22 Nov 2022 02:36