(1) Mathematics Department, UMIST, P.O. Box 88, Manchester, M60 1QD, UK;(2) Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, LA1 4YF, UK
Abstract:
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.