General over-relaxation Markov chain Monte Carlo algorithms for Gaussian densities (Q5937057)

The authors consider sampling \(n\)-dimensional random vectors from a Gaussian density with a positive definite covariance matrix. They examine various blocking and over-relaxation strategies which lead to a considerable reduction in the number of iterations required for convergence. These algorithms are illustrated using an image analysis problem.