Return to search

Smoothed Transformed Density Rejection

There are situations in the framework of quasi-Monte Carlo integration where nonuniform low-discrepancy sequences are required. Using the inversion method for this task usually results in the best performance in terms of the integration errors. However, this method requires a fast algorithm for evaluating the inverse of the cumulative distribution function which is often not available. Then a smoothed version of transformed density rejection is a good alternative as it is a fast method and its speed hardly depends on the distribution. It can easily be adjusted such that it is almost as good as the inversion method. For importance sampling it is even better to use the hat distribution as importance distribution directly. Then the resulting algorithm is as good as using the inversion method for the original importance distribution but its generation time is much shorter. / Series: Preprint Series / Department of Applied Statistics and Data Processing

Identiferoai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:epub-wu-01_9f8
Date January 2003
CreatorsLeydold, Josef, Hörmann, Wolfgang
PublisherDepartment of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business
Source SetsWirtschaftsuniversität Wien
LanguageEnglish
Detected LanguageEnglish
TypePaper, NonPeerReviewed
Formatapplication/pdf
Relationhttp://epub.wu.ac.at/228/

Page generated in 0.0021 seconds