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On the use of marginal posteriors in marginal likelihood estimation via importance sampling.

Perrakis, Konstantinos and Ntzoufras, Ioannis and Tsionas, Efthymios G. (2014) 'On the use of marginal posteriors in marginal likelihood estimation via importance sampling.', Computational statistics & data analysis., 77 . pp. 54-69.


The efficiency of a marginal likelihood estimator where the product of the marginal posterior distributions is used as an importance sampling function is investigated. The approach is generally applicable to multi-block parameter vector settings, does not require additional Markov Chain Monte Carlo (MCMC) sampling and is not dependent on the type of MCMC scheme used to sample from the posterior. The proposed approach is applied to normal regression models, finite normal mixtures and longitudinal Poisson models, and leads to accurate marginal likelihood estimates.

Item Type:Article
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Publisher statement:© 2014 This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Date accepted:08 March 2014
Date deposited:08 October 2019
Date of first online publication:19 March 2014
Date first made open access:No date available

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