Statistics and Computing

, Volume 15, Issue 4, pp 315–327 | Cite as

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

  • Peter NealEmail author
  • Gareth Roberts


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.


stochastic epidemic models bernoulli random graphs non-centered and partially non-centered MCMC algorithms data augmentation 


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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Mathematics DepartmentUMISTManchesterUK
  2. 2.Department of Mathematics and StatisticsFylde College, Lancaster UniversityLancasterUK

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