A new proof of geometric convergence for the adaptive generalized weighted analog sampling (GWAS) method

Kong Rong () and Spanier Jerome ()
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Kong Rong: Hyundai Capital America, 3161 Michelson Drive, Suite 1900, Irvine, CA 92612, United States of America
Spanier Jerome: Beckman Laser Institute and Medical Clinic, 1002 Health Science Road E., University of California, Irvine, California 92612, United States of America

Monte Carlo Methods and Applications, 2016, vol. 22, issue 3, 161-196

Abstract: Generalized Weighted Analog Sampling is a variance-reducing method for solving radiative transport problems that makes use of a biased (though asymptotically unbiased) estimator. The introduction of bias provides a mechanism for combining the best features of unbiased estimators while avoiding their limitations. In this paper we present a new proof that adaptive GWAS estimation based on combining the variance-reducing power of importance sampling with the sampling simplicity of correlated sampling yields geometrically convergent estimates of radiative transport solutions. The new proof establishes a stronger and more general theory of geometric convergence for GWAS.

Keywords: Monte Carlo methods; radiative transport; generalized weighted analog; geometric convergence (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1515/mcma-2016-0110

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