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Bayes Factors for Partially Observed Stochastic Epidemic Models

Alharthi, Muteb; Kypraios, Theodore; O'Neill, Philip D.


Muteb Alharthi

Professor of Applied Probability


We consider the problem of model choice for stochastic epidemic models given partial observation of a disease outbreak through time. Our main focus is on the use of Bayes factors. Although Bayes factors have appeared in the epidemic modelling literature before, they can be hard to compute and little attention has been given to fundamental questions concerning their utility. In this paper we derive analytic expressions for Bayes factors given complete observation through time, which suggest practical guidelines for model choice problems. We adapt the power posterior method for computing Bayes factors so as to account for missing data and apply this approach to partially observed epidemics. For comparison, we
also explore the use of a deviance information criterion for missing data scenarios. The methods are illustrated via examples involving both simulated and real data.


Alharthi, M., Kypraios, T., & O'Neill, P. D. (2019). Bayes Factors for Partially Observed Stochastic Epidemic Models. Bayesian Analysis, 14(3), 927-956.

Journal Article Type Article
Acceptance Date Oct 23, 2018
Publication Date 2019-09
Deposit Date Nov 1, 2018
Publicly Available Date Jan 11, 2019
Journal Bayesian Analysis
Print ISSN 1936-0975
Electronic ISSN 1931-6690
Publisher International Society for Bayesian Analysis
Peer Reviewed Peer Reviewed
Volume 14
Issue 3
Pages 927-956
Keywords Bayes factor; power posterior; stochastic epidemic model
Public URL
Publisher URL


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