Journal
EPIDEMIOLOGY AND INFECTION
Volume 136, Issue 12, Pages 1599-1605Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0950268808000526
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We considered a Bayesian analysis for the prevalence of tuberculosis cases in New York City from 1970 to 2000. This counting dataset presented two change-points during this period. We modelled this counting dataset considering non-homogeneous Poisson processes in the presence of the two-change points. A Bayesian analysis for the data is considered using Markov chain Monte Carlo methods. Simulated Gibbs samples for the parameters of interest were obtained Using WinBugs software.
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