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dc.contributor.authorMoraes, Alvaro
dc.date.accessioned2017-06-05T08:35:49Z
dc.date.available2017-06-05T08:35:49Z
dc.date.issued2015-01-07
dc.identifier.urihttp://hdl.handle.net/10754/624109
dc.description.abstractStochastic reaction networks (SRNs) is a class of continuous-time Markov chains intended to describe, from the kinetic point of view, the time-evolution of chemical systems in which molecules of different chemical species undergo a finite set of reaction channels. This talk is based on articles [4, 5, 6], where we are interested in the following problem: given a SRN, X, defined though its set of reaction channels, and its initial state, x0, estimate E (g(X(T))); that is, the expected value of a scalar observable, g, of the process, X, at a fixed time, T. This problem lead us to define a series of Monte Carlo estimators, M, such that, with high probability can produce values close to the quantity of interest, E (g(X(T))). More specifically, given a user-selected tolerance, TOL, and a small confidence level, η, find an estimator, M, based on approximate sampled paths of X, such that, P (|E (g(X(T))) − M| ≤ TOL) ≥ 1 − η; even more, we want to achieve this objective with near optimal computational work. We first introduce a hybrid path-simulation scheme based on the well-known stochastic simulation algorithm (SSA)[3] and the tau-leap method [2]. Then, we introduce a Multilevel Monte Carlo strategy that allows us to achieve a computational complexity of order O(T OL−2), this is the same computational complexity as in an exact method but with a smaller constant. We provide numerical examples to show our results.
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/869e9e5ed1424f18ac2ca76f04709c421d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
dc.titleHybrid Multilevel Monte Carlo Simulation of Stochastic Reaction Networks
dc.typePresentation
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.dateJanuary 6-9, 2015
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
dc.conference.locationKAUST
kaust.personMoraes, Alvaro
refterms.dateFOA2018-06-14T03:15:52Z


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