Bayesian inference for partially observed stochastic epidemics |
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Authors: | P D O'Neill & G O Roberts |
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Institution: | University of Bradford, UK,;University of Cambridge, UK |
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Abstract: | The analysis of infectious disease data is usually complicated by the fact that real life epidemics are only partially observed. In particular, data concerning the process of infection are seldom available. Consequently, standard statistical techniques can become too complicated to implement effectively. In this paper Markov chain Monte Carlo methods are used to make inferences about the missing data as well as the unknown parameters of interest in a Bayesian framework. The methods are applied to real life data from disease outbreaks. |
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Keywords: | Bayesian inference Epidemic General stochastic epidemic Gibbs sampler Hastings algorithm Markov chain Monte Carlo methods Reed–Frost epidemic |
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