  Bayesian regression remains a simple but effective tool based on Bayesian
inference techniques. For large-scale applications, with complicated posterior
distributions, Markov Chain Monte Carlo methods are applied. To improve the
well-known computational burden of Markov Chain Monte Carlo approach for
Bayesian regression, we developed a multilevel Gibbs sampler for Bayesian
regression of linear mixed models. The level hierarchy of data matrices is
created by clustering the features and/or samples of data matrices.
Additionally, the use of correlated samples is investigated for variance
reduction to improve the convergence of the Markov Chain. Testing on a diverse
set of data sets, speed-up is achieved for almost all of them without
significant loss in predictive performance.

Computation;Machine Learning;Numerical Analysis;Numerical Analysis;Machine Learning