O'Neil, P. D. and Roberts, G. O. (1999) Bayesian inference for partially observed stochastic epidemics. Journal of the Royal Statistical Society: Series A (Statistics in Society), 162 (1). pp. 121-129. ISSN 0964-1998Full text not available from this repository.
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.
|Journal or Publication Title:||Journal of the Royal Statistical Society: Series A (Statistics in Society)|
|Uncontrolled Keywords:||Bayesian inference • Epidemic • General stochastic epidemic • Gibbs sampler • Hastings algorithm • Markov chain Monte Carlo methods • Reed–Frost epidemic|
|Subjects:||?? qa ??|
|Departments:||Faculty of Science and Technology > Lancaster Environment Centre|
|Deposited On:||18 Nov 2008 15:01|
|Last Modified:||27 Apr 2017 01:15|
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