Iterative algorithms for non-conditional and conditional simulation of Gaussian random vectors
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2020Metadata
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Arroyo, Daisy
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Iterative algorithms for non-conditional and conditional simulation of Gaussian random vectors
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Abstract
The conditional simulation of Gaussian random vectors is widely used in geostatistical applications to quantify uncertainty in regionalized phenomena that have been observed at finitely many sampling locations. Two iterative algorithms are presented to deal with such a simulation. The first one is a variation of the propagative version of the Gibbs sampler aimed at simulating the random vector without any conditioning data. The novelty of the presented algorithm stems from the introduction of a relaxation parameter that, if adequately chosen, allows quickening the rates of convergence and mixing of the sampler. The second algorithm is meant to convert the non-conditional simulation into a conditional one, based on the successive over-relaxation method. Again, a relaxation parameter allows quickening the convergence in distribution to the desired conditional random vector. Both algorithms are applicable in a very general setting and avoid the pivoting, inversion, square rooting or decomposition of the variance-covariance matrix of the vector to be simulated, thus reduce the computation costs and memory requirements with respect to other discrete geostatistical simulation approaches.
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National Agency for Research and Development of Chile, through Project ANID/CONICYT FONDECYT INICIACIN EN INVESTIGACIN
11170529
National Agency for Research and Development of Chile, through Project ANID REC CONCURSO NACIONAL INSERCIN EN LA ACADEMIA
National Agency for Research and Development of Chile, through Project CONVOCATORIA 2016
PAI79160084
National Agency for Research and Development of Chile, through Project ANID/CONICYT
PIA AFB180004
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Stochastic Environmental Research and Risk Assessment (2020) 34:1523–1541
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