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Bayesian inference for partially observed stochastic epidemics
Author(s) -
O’Neill P. D.,
Roberts G. O.
Publication year - 1999
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/1467-985x.00125
Subject(s) - markov chain monte carlo , computer science , bayesian probability , statistical inference , missing data , inference , bayesian inference , data mining , infectious disease (medical specialty) , econometrics , markov chain , machine learning , statistics , artificial intelligence , mathematics , disease , medicine , pathology
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.