Marginal likelihood estimation via power posteriors

Friel, Nial & Pettitt, Anthony N. (2008) Marginal likelihood estimation via power posteriors. Journal of the Royal Statistical Society Series B (Statistical Methodology), 70(3), pp. 589-607.

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Model choice plays an increasingly important role in statistics. From a Bayesian perspective a crucial goal is to compute the marginal likelihood of the data for a given model. However, this is typically a difficult task since it amounts to integrating over all model parameters. The aim of the paper is to illustrate how this may be achieved by using ideas from thermodynamic integration or path sampling. We show how the marginal likelihood can be computed via Markov chain Monte Carlo methods on modified posterior distributions for each model. This then allows Bayes factors or posterior model probabilities to be calculated. We show that this approach requires very little tuning and is straightforward to implement. The new method is illustrated in a variety of challenging statistical settings.

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86 citations in Web of Science®

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ID Code: 17226
Item Type: Journal Article
Refereed: Yes
Additional Information: Self-archiving of the author-version in institutional repositories is not currently supported by this publisher. For more information, please refer to the journal's website (see hypertext link) or contact the author.
Keywords: Bayes Factor, Hidden Markov Model, Model Choice, Regression, Survival Analysis
DOI: 10.1111/j.1467-9868.2007.00650.x
ISSN: 1369-7412
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2008 John Wiley and Sons
Deposited On: 30 Jan 2009 01:08
Last Modified: 29 Feb 2012 13:47

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