## Estimating the integrated likelihood via posterior simulation using the harmonic mean identity (2007)

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Venue: | Bayesian Statistics |

Citations: | 24 - 2 self |

### BibTeX

@INPROCEEDINGS{Raftery07estimatingthe,

author = {Adrian E. Raftery and Michael A. Newton and Jaya M. Satagopan and Pavel N. Krivitsky},

title = {Estimating the integrated likelihood via posterior simulation using the harmonic mean identity},

booktitle = {Bayesian Statistics},

year = {2007},

pages = {1--45}

}

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### Abstract

The integrated likelihood (also called the marginal likelihood or the normalizing constant) is a central quantity in Bayesian model selection and model averaging. It is defined as the integral over the parameter space of the likelihood times the prior density. The Bayes factor for model comparison and Bayesian testing is a ratio of integrated likelihoods, and the model weights in Bayesian model averaging are proportional to the integrated likelihoods. We consider the estimation of the integrated likelihood from posterior simulation output, aiming at a generic method that uses only the likelihoods from the posterior simulation iterations. The key is the harmonic mean identity, which says that the reciprocal of the integrated likelihood is equal to the posterior harmonic mean of the likelihood. The simplest estimator based on the identity is thus the harmonic mean of the likelihoods. While this is an unbiased and simulation-consistent estimator, its reciprocal can have infinite variance and so it is unstable in general. We describe two methods for stabilizing the harmonic mean estimator. In the first one, the parameter space is reduced in such a way that the modified estimator involves a harmonic mean of heavier-tailed densities, thus resulting in a finite variance estimator. The resulting

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