Monte Carlo Methods and Applications
Managing Editor: Sabelfeld, Karl K.
Editorial Board Member: Binder, Kurt / Bouleau, Nicolas / Chorin, Alexandre J. / Dimov, Ivan / Dubus, Alain / Egorov, Alexander D. / Ermakov, Sergei M. / Halton, John H. / Heinrich, Stefan / Kalos, Malvin H. / Lepingle, D. / Makarov, Roman / Mascagni, Michael / Mathe, Peter / Niederreiter, Harald / Platen, Eckhard / Sawford, Brian R. / Schmid, Wolfgang Ch. / Schoenmakers, John / Simonov, Nikolai A. / Sobol, Ilya M. / Spanier, Jerry / Talay, Denis
SCImago Journal Rank (SJR) 2014: 0.205
Source Normalized Impact per Paper (SNIP) 2014: 0.374
Impact per Publication (IPP) 2014: 0.368
Mathematical Citation Quotient (MCQ) 2014: 0.19
Volume 22 (2016)
Volume 21 (2015)
Volume 20 (2014)
Volume 19 (2013)
Volume 18 (2012)
Volume 17 (2011)
Volume 16 (2010)
Volume 15 (2009)
Volume 14 (2008)
Volume 13 (2008)
Volume 12 (2006)
Volume 11 (2005)
Volume 10 (2004)
Volume 9 (2003)
Volume 8 (2002)
Volume 6 (2000)
Volume 5 (1999)
Volume 4 (1998)
Volume 3 (1997)
Volume 2 (1996)
Towards automatic global error control: Computable weak error expansion for the tau-leap method
1Mathematical and Computer Sciences and Engineering (MCSE), King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
Citation Information: Monte Carlo Methods and Applications. Volume 17, Issue 3, Pages 233–278, ISSN (Online) 1569-3961, ISSN (Print) 0929-9629, DOI: 10.1515/mcma.2011.011, October 2011
- Published Online:
This work develops novel error expansions with computable leading order terms for the global weak error in the tau-leap discretization of pure jump processes arising in kinetic Monte Carlo models. Accurate computable a posteriori error approximations are the basis for adaptive algorithms, a fundamental tool for numerical simulation of both deterministic and stochastic dynamical systems. These pure jump processes are simulated either by the tau-leap method, or by exact simulation, also referred to as dynamic Monte Carlo, the Gillespie Algorithm or the Stochastic Simulation Slgorithm. Two types of estimates are presented: an a priori estimate for the relative error that gives a comparison between the work for the two methods depending on the propensity regime, and an a posteriori estimate with computable leading order term.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.