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Improving Stochastic Relaxation for Gussian Random Fields

Published online by Cambridge University Press:  27 July 2009

Piero Barone  and
Arnolodo Frigessi
Piero Barone
Affiliation:
Istituto per le Appilcazioni del Calcola Consigio Nazionale delle Ricerche Viale del Pollclinico 137, 00161 Rome, Italy
Arnolodo Frigessi
Affiliation:
Istituto per le Appilcazioni del Calcola Consigio Nazionale delle Ricerche Viale del Pollclinico 137, 00161 Rome, Italy
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Abstract

In this paper, we are concerned with the simulation of Gaussian random fields by means of iterative stochastic algorithms, which are compared in terms of rate of convergence. A parametrized class of algorithms, which includes stochastic relaxation (Gibbs sampler), is proposed and its convergence properties are established. A suitable choice for the parameter improves the rate of convergence with respect to stochastic relaxation for special classes of covariance matrices. Some examples and numerical experiments are given.

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Type
Articles
Information
Probability in the Engineering and Informational Sciences , Volume 4 , Issue 3 , July 1990 , pp. 369 - 389
Copyright
Copyright © Cambridge University Press 1990

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References

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