Bayesian Multimodel Inference by RJMCMC: A Gibbs Sampling Approach
Abstract
Bayesian multimodel inference treats a set of candidate models as the sample space of a latent categorical random variable, sampled once; the data at hand are modeled as having been generated according to the sampled model. Model selection and model averaging are based on the posterior probabilities for the model set. Reversible-jump Markov chain Monte Carlo (RJMCMC) extends ordinary MCMC methods to this meta-model. We describe a version of...
Paper Details
Title
Bayesian Multimodel Inference by RJMCMC: A Gibbs Sampling Approach
Published Date
Aug 1, 2013
Journal
Volume
67
Issue
3
Pages
150 - 156
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