Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 1 of 1
Full-Text Articles in Entire DC Network
Estimating The Integrated Likelihood Via Posterior Simulation Using The Harmonic Mean Identity, Adrian E. Raftery, Michael A. Newton, Jaya M. Satagopan, Pavel N. Krivitsky
Estimating The Integrated Likelihood Via Posterior Simulation Using The Harmonic Mean Identity, Adrian E. Raftery, Michael A. Newton, Jaya M. Satagopan, Pavel N. Krivitsky
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
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 …