Conditional simulation from highly structured Gaussian systems, with application to blocking-MCMC for the Bayesian analysis of very large linear models

This paper examines strategies for simulating exactly from large Gaussian linear models conditional on some Gaussian observations. Local computation strategies based on the conditional independence structure of the model are developed in order to reduced costs associated with storage and computation. Application of these algorithms to simulation from nested hierarchical linear models is considered, and the construction of efficient MCMC schemes for Bayesian inference in high-dimensional linear models is outlined.