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.
Full text not available from this repository.Official URL: http://dx.doi.org/10.1111/1467-985X.00125
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.
| Item Type: | Article |
|---|---|
| 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: | Q Science > QA Mathematics |
| Departments: | Faculty of Science and Technology > Lancaster Environment Centre |
| ID Code: | 19426 |
| Deposited By: | ep_ss_importer |
| Deposited On: | 18 Nov 2008 15:01 |
| Refereed?: | Yes |
| Published?: | Published |
| Last Modified: | 26 Jul 2012 15:29 |
| Identification Number: | |
| URI: | http://eprints.lancs.ac.uk/id/eprint/19426 |
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