Bayesian model choice via mixture distributions with application to epidemics and population process models - O'Neill, Philip D.; Kypraios, Theodore (University of Nottingham, 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 models and stochastic epidemics.