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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.

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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