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

O'Neill, Philip D.; Kypraios, Theodore

Authors

PHILIP O'NEILL philip.oneill@nottingham.ac.uk
Professor of Applied Probability



Abstract

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.

Citation

O'Neill, P. D., & Kypraios, T. Bayesian model choice via mixture distributions with application to epidemics and population process models. University of Nottingham

Book Type Monograph
Deposit Date Nov 28, 2014
Peer Reviewed Not Peer Reviewed
Public URL http://eprints.nottingham.ac.uk/id/eprint/27861
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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