Improving the rejection sampling method in quasi-Monte Carlo methods
type: Item
instance of: scholarly article
title: Improving the rejection sampling method in quasi-Monte Carlo methods
zbMATH Open document ID: 0959.65006
DOI: 10.1016/S0377-0427(99)00194-6
author: Xiaoqun Wang
published in: Journal of Computational and Applied Mathematics
publication date: 2000-12-21T00:00:00Z
review text: The authors investigate and improve the performance of the deterministic rejection method in quasi-Monte Carlo (QMC) methods. They study the quality of the point sets generated by the standard deterministic rejection method. It turns out that the quality is closely related to the problem of QMC integration of characteristic functions, whose accuracy may be lost due to the discontinuity of the characteristic functions. Further on, in a rather general case, a method of smoothing characteristic functions is proposed. The characteristic functions are replaced by continuous ones, which have the same value of integral. This smoothing method is used to improve the rejection method. An extended smoothed version is described and numerical experiments are given.
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 65D32
zbMATH DE Number: 1419819
zbMATH Keywords: quasi Monte Carlo methods
zbMATH Keywords: low discrepancy sequences
zbMATH Keywords: F-discrepancy
zbMATH Keywords: star-discrepancy
zbMATH Keywords: rejection method
zbMATH Keywords: rejection sampling
zbMATH Keywords: numerical experiments
reviewed by: Jaromír Antoch
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 1419819