# Bayesian model choice via mixture distributions with application to epidemics and population process models

@article{ONeill2014BayesianMC, title={Bayesian model choice via mixture distributions with application to epidemics and population process models}, author={P. O'Neill and T. Kypraios}, journal={arXiv: Methodology}, year={2014} }

We consider Bayesian model choice for the setting where the observed data are partially observed realisations of a stochastic population process. A new method for computing Bayes factors is described which avoids the need to use reversible jump approaches. The key idea is to perform inference for a hypermodel in which the competing models are components of a mixture distribution. The method itself has fairly general applicability. The methods are illustrated using simple population process… CONTINUE READING

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Bayesian inference and model selection for partially observed stochastic epidemics.

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