- Bayesian Anal. (2017), 23 pages.
Efficient Model Comparison Techniques for Models Requiring Large Scale Data Augmentation
Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. In this paper we offer a simple solution by devising an algorithm which combines MCMC and importance sampling to obtain computationally efficient estimates of the marginal likelihood which can then be used to compare the models. The algorithm is successfully applied to a longitudinal epidemic data set, where calculating the marginal likelihood is made more challenging by the presence of large amounts of missing data. In this context, our importance sampling approach is shown to outperform existing methods for computing the marginal likelihood.
Bayesian Anal. (2017), 23 pages.
First available in Project Euclid: 29 April 2017
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Touloupou, Panayiota; Alzahrani, Naif; Neal, Peter; Spencer, Simon E. F.; McKinley, Trevelyan J. Efficient Model Comparison Techniques for Models Requiring Large Scale Data Augmentation. Bayesian Anal., advance publication, 29 April 2017. doi:10.1214/17-BA1057. https://projecteuclid.org/euclid.ba/1493431262
- Supplementary material: Efficient model comparison techniques for models requiring large scale data augmentation.