2014/11/28 by Philip D. O’Neill, Philip D. O'Neill, Theodore Kypraios +2
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Distribution Estimation and Applications #Statistical Methods and Bayesian Inference #stat.CO #stat.ME
paper · pdf · doi:10.48550/arxiv.1411.7888
openalex publication_date 2014/11/28 · arxiv created 2016/02/14 · arxiv updated 2016/02/16 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
We describe a new method for evaluating Bayes factors. The key idea is to introduce a hypermodel in which the competing models are components of a mixture distribution. Inference for the mixing probabilities then yields estimates of the Bayes factors. Our motivation is the setting where the observed data are a partially observed realisation of a stochastic population process, although the methods have far wider applicability. The methods allow for missing data and for parameters to be shared between models. Illustrative examples including epidemics, population processes and regression models are given, showing that the methods are competitive compared to existing approaches.