Deakin home > Deakin University Library > Deakin Research Online > Class library ranlip for multivariate nonuniform random variate generation
Openly accessible

Class library ranlip for multivariate nonuniform random variate generation

Beliakov, Gleb 2005, Class library ranlip for multivariate nonuniform random variate generation, Computer physics communications, vol. 170, no. 1, pp. 93-108.

Attached Files (Some files may be inaccessible until you login with your Deakin Research Online credentials)
Name Description MIMEType Size Downloads
beliakov-CPP-2005.pdf Author post print application/pdf 331.76KB 89

Title Class library ranlip for multivariate nonuniform random variate generation
Author(s) Beliakov, Gleb
Journal name Computer physics communications
Volume number 170
Issue number 1
Start page 93
End page 108
Publisher North-Holland Pub. Co.,
Place of publication Amsterdam, Netherlands
Publication date 2005-07-15
ISSN 0010-4655
Keyword(s) nonuniform random variate generation
acceptance/rejection
multimodal distributions
multivariate Lipschitz densities
Summary This paper describes generation of nonuniform random variates from Lipschitz-continuous densities using acceptance/rejection, and the class library ranlip which implements this method. It is assumed that the required distribution has Lipschitz-continuous density, which is either given analytically or as a black box. The algorithm builds a piecewise constant upper approximation to the density (the hat function), using a large number of its values and subdivision of the domain into hyperrectangles. The class library ranlip provides very competitive preprocessing and generation times, and yields small rejection constant, which is a measure of efficiency of the generation step. It exhibits good performance for up to five variables, and provides the user with a black box nonuniform random variate generator for a large class of distributions, in particular, multimodal distributions. It will be valuable for researchers who frequently face the task of sampling from unusual distributions, for which specialized random variate generators are not available.
Language eng
Field of Research 010201 Approximation Theory and Asymptotic Methods
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2005, Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003045

Document type: Journal Article
Collections: School of Information Technology
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in Deakin Research Online is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Access Statistics: 418 Abstract Views, 91 File Downloads  -  Detailed Statistics
Created: Mon, 07 Jul 2008, 08:41:46 EST