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Efficient model comparison techniques for models requiring large scale data augmentation
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Touloupou, Panayiota, Alzahrani, Naif, Neal, Peter, Spencer, Simon E. F. and McKinley, Trevelyan J. (2018) Efficient model comparison techniques for models requiring large scale data augmentation. Bayesian Analysis, 13 (2). pp. 437-459. doi:10.1214/17-BA1057
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Official URL: https://doi.org/10.1214/17-BA1057
Abstract
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.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics | ||||||||
| Divisions: | Faculty of Science > Statistics | ||||||||
| Library of Congress Subject Headings (LCSH): | Markov processes, Bayesian statistical decision theory, Monte Carlo method | ||||||||
| Journal or Publication Title: | Bayesian Analysis | ||||||||
| Publisher: | International Society for Bayesian Analysis | ||||||||
| ISSN: | 1931-6690 | ||||||||
| Official Date: | 2018 | ||||||||
| Dates: |
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| Date of first compliant deposit: | 27 April 2017 | ||||||||
| Volume: | 13 | ||||||||
| Number: | 2 | ||||||||
| Page Range: | pp. 437-459 | ||||||||
| DOI: | 10.1214/17-BA1057 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Funder: | University of Warwick PhD studentship, Saudi Arabia. Wizārat al-Taʻlīm al-ʻĀlī |
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