Using the Gibbs sampler for conditional simulation of Gaussian-based random fields
Computers & Geosciences
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper addresses the problem of simulating a Gaussian random vector with zero mean and given variance-covariance matrix, without conditioning constraints. Variants of the Gibbs sampler algorithm are presented, based on the proposal by Galli and Gao, which do not require inverting the variance-covariance matrix and therefore allow considerable time savings. Numerical experiments are performed to check the accuracy of the algorithm and to determine implementation parameters (in particular, the updating and blocking strategies) that increase the rates of convergence and mixing.