Open Access
June 2018 Efficient Model Comparison Techniques for Models Requiring Large Scale Data Augmentation
Panayiota Touloupou, Naif Alzahrani, Peter Neal, Simon E. F. Spencer, Trevelyan J. McKinley
Bayesian Anal. 13(2): 437-459 (June 2018). DOI: 10.1214/17-BA1057


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.


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Panayiota Touloupou. Naif Alzahrani. Peter Neal. Simon E. F. Spencer. Trevelyan J. McKinley. "Efficient Model Comparison Techniques for Models Requiring Large Scale Data Augmentation." Bayesian Anal. 13 (2) 437 - 459, June 2018.


Published: June 2018
First available in Project Euclid: 29 April 2017

zbMATH: 06989955
MathSciNet: MR3780430
Digital Object Identifier: 10.1214/17-BA1057

Keywords: epidemics , marginal likelihood , Model evidence , Model selection

Vol.13 • No. 2 • June 2018
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