PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Stability of the Gibbs sampler for Bayesian hierarchical models
Omiros Papaspiliopoulos and Gareth Roberts
Annals of Statistics Volume 36, 2008.


We characterize the convergence of the Gibbs sampler which samples from the joint posterior distribution of parameters and missing data in hierarchical linear models with arbitrary symmetric error distributions. We show that the convergence can be uniform, geometric or subgeometric depending on the relative tail behavior of the error distributions, and on the parametrization chosen. Our theory is applied to characterize the convergence of the Gibbs sampler on latent Gaussian process models. We indicate how the theoretical framework we introduce will be useful in analyzing more complex models.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:5005
Deposited By:Omiros Papaspiliopoulos
Deposited On:24 March 2009