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On the convergence of quasi‐random sampling importance resampling
Author(s) -
Cools Ronald,
Vandewoestyne Bart
Publication year - 2007
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.200700083
Subject(s) - resampling , monte carlo method , convergence (economics) , multiplicative function , sampling (signal processing) , mathematics , importance sampling , rejection sampling , computer science , algorithm , statistics , markov chain monte carlo , hybrid monte carlo , mathematical analysis , filter (signal processing) , economics , computer vision , economic growth
The Sampling/Importance Resampling (SIR) algorithm can be used for generating representative point sets from a distribution known up to a multiplicative constant. Moreover, the Quasi‐random Sampling Importance Resampling (QSIR) scheme, based on quasi‐Monte Carlo methods, is a recent modification of the SIR algorithm and was empirically shown to have better convergence. We present error convergence results for QSIR that we obtained using quasi‐Monte Carlo theory. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)