✦ LIBER ✦

On Bayesian calculations for mixture likelihoods and priors

✍ Scribed by Robert E. Weiss; Meehyung Cho; Michael Yanuzzi

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 121 KB
Volume: 18
Category: Article
ISSN: 0277-6715
DOI: 10.1002/(sici)1097-0258(19990630)18:12<1555::aid-sim145>3.0.co;2-x

No coin nor oath required. For personal study only.

✦ Synopsis

We present methodology for calculating Bayes factors between models as well as posterior probabilities of the models when the indicator variables of the models are integrated out of the posterior before Markov chain Monte Carlo (MCMC) computations. Standard methodology would include the indicator functions as part of the MCMC computations. We demonstrate that our methodology can give substantially greater accuracy than the traditional approach. We illustrate the methodology using the model selection prior of George and McCulloch applied to logistic regression and to a mixture model for observations in a hierarchical random e ects model.

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