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dc.contributor.authorBen Hammouda, Chiheb
dc.contributor.authorMoraes, Alvaro
dc.contributor.authorTempone, Raul
dc.date.accessioned2019-01-13T05:49:46Z
dc.date.available2019-01-13T05:49:46Z
dc.date.issued2016-08-15
dc.identifier.urihttp://hdl.handle.net/10754/630797
dc.description.abstractIn biochemically reactive systems with small copy numbers of one or more reactant molecules, the dynamics is dominated by stochastic effects. To approximate those systems, discrete state-space and stochastic simulation approaches have been shown to be more relevant than continuous state-space and deterministic ones. In systems characterized by having simultaneously fast and slow timescales, existing discrete space-state stochastic path simulation methods, such as the stochastic simulation algorithm (SSA) and the explicit tau-leap method, can be very slow. Implicit approximations have been developed to improve numerical stability and provide efficient simulation algorithms for those systems. Here, we propose an efficient Multilevel Monte Carlo (MLMC) method in the spirit of the work by Anderson and Higham (2012). This method uses split-step implicit tau-leap (SSI-TL) at levels where the explicit-TL method is not applicable due to numerical stability issues. We present numerical examples that illustrate the performance of the proposed method.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.titleMultilevel hybrid split-step implicit tau-leap
dc.typePresentation
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.date14-19/08/2016
dc.conference.nameMCQMC Conference
dc.conference.locationStanford University
refterms.dateFOA2019-01-13T05:49:46Z


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