An Object-Oriented Random-Number Package with Many Long Streams and Substreams

By Pierre L'Ecuyer, Richard J. Simard, E. Chen, W. Kelton. Operational Research, 2002

354 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a661bb8c56417573301af6fc464cb93f6520bcd0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

In Computer Vision and Pattern Recognition

Citations in the corpus

Monte Carlo and Quasi-Monte Carlo Sampling (2009, background): “92), portability (that is, the sequence output by the generator does not depend on the programming language, compiler, or machine used), ease of implementation, and jumping ahead capabilities, which are useful when the sequence output by the generator is subdivided into substreams, so that one can “”
Random Number Generation (2008, methodology): “Extensive experiments with LCGs, MRGs, and LFSR generators confirms that this is actually what happens with these RNGs (L’Ecuyer and Hellekalek, 1998, L’Ecuyer, 2001, L’Ecuyer et al., 2002).”
Knot selection for least-squares and penalized splines (2013, methodology): “We used the RngStream package of [35] to assure that each process received an independent random number stream.”
Markov chain Monte Carlo: Can we trust the third significant figure? (2007, methodology): “Finally, we note that this simulation study was conducted on a Linux cluster using R (Ihaka and Gentleman (1996)), an MCMC package for spatial modeling, spBayes (Finley, Banerjee and Carlin (2007)) and the parallel random number generator package rlecuyer (L’Ecuyer et al., 2002).”
The NS Manual (Formerly NS Notes and Documentation (2002, neutral): “For more i nformation, see [16] and the source code found in tools/rng.”
On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods (2009, methodology): “The thread can jump to the start of its block of numbers using a “skip-ahead” algorithm which enables it to skip n places in O (log n ) operations (e.g. see L’Ecuyer et al. (2002)).”
Bayesian computation: a perspective on the current state, and sampling backwards and forwards (2015, methodology): “The resulting algorithms are termed Manifold MALA (MMALA), Simpliﬁed MMALA Girolami and Calderhead (2010), and position-dependent MALA (PMALA) Xifara et al. (2014), and diﬀer in implementation cost, depending on how precise is the use they make of versions of equation (3).”