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General over-relaxation Markov chain Monte Carlo algorithms for Gaussian densities. (English) Zbl 0987.60087
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.

MSC:
60J22 Computational methods in Markov chains
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