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Efficient model comparison techniques for models requiring large scale data augmentation

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>03/2018
<mark>Journal</mark>Bayesian Analysis
Issue number2
Number of pages23
Pages (from-to)437-459
Publication StatusPublished
Early online date29/04/17
<mark>Original language</mark>English


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.