文章基本信息

标题：Bayes Factors for Partially Observed Stochastic Epidemic Models
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作者：Muteb Alharthi ; Theodore Kypraios ; Philip D. O’Neill 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2019
卷号：14
期号：3
页码：907-936
DOI：10.1214/18-BA1134
语种：English
出版社：International Society for Bayesian Analysis
摘要：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.
关键词：Bayes factor; power posterior; stochastic epidemic model.