Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
Gibbs 抽样器的协方差结构：估计量与增广方案比较
We study the covariance structure of a Markov chain generated by the Gibbs sampler, with emphasis on data augmentation. When applied to a Bayesian missing data problem, the Gibbs sampler produces two natural approximations for the posterior distribution of the parameter vector: the empirical distribution based on the sampled values of the parameter vector, and a mixture of complete data posteriors. We prove that Rao-Blackwellization causes a one-lag delay for the…