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Posterior model probabilities via path‐based pairwise priors
Author(s) -
Berger James O.,
Molina German
Publication year - 2005
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2005.00275.x
Subject(s) - prior probability , pairwise comparison , posterior probability , model selection , bayesian probability , mathematics , selection (genetic algorithm) , focus (optics) , path (computing) , bayesian inference , computer science , variable (mathematics) , statistics , mathematical optimization , econometrics , artificial intelligence , physics , optics , programming language , mathematical analysis
We focus on Bayesian model selection for the variable selection problem in large model spaces. The challenge is to search the huge model space adequately, while accurately approximating model posterior probabilities for the visited models. The issue of choice of prior distributions for the visited models is also important.