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dc.contributor.authorKarlsson, Peer Jesper
dc.contributor.authorTempone, Raul
dc.date.accessioned2015-08-03T09:02:29Z
dc.date.available2015-08-03T09:02:29Z
dc.date.issued2011-01
dc.identifier.issn09299629
dc.identifier.doi10.1515/MCMA.2011.011
dc.identifier.urihttp://hdl.handle.net/10754/561695
dc.description.abstractThis 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. © de Gruyter 2011.
dc.publisherWalter de Gruyter GmbH
dc.relation.urlhttp://arxiv.org/abs/arXiv:1004.2948v3
dc.subjectA posteriori error estimates
dc.subjectBackward dual functions
dc.subjectError estimation
dc.subjectMarkov chain
dc.subjectReaction networks
dc.subjectTau-leap
dc.subjectWeak approximation
dc.titleTowards automatic global error control: Computable weak error expansion for the tau-leap method
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentStochastic Numerics Research Group
dc.identifier.journalMonte Carlo Methods and Applications
dc.identifier.arxividarXiv:1004.2948
kaust.personKarlsson, Peer Jesper
kaust.personTempone, Raul
dc.date.posted2010-04-17


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