dc.contributor.author Moraes, Alvaro dc.date.accessioned 2017-06-05T08:35:49Z dc.date.available 2017-06-05T08:35:49Z dc.date.issued 2015-01-07 dc.identifier.uri http://hdl.handle.net/10754/624109 dc.description.abstract Stochastic 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.url http://mediasite.kaust.edu.sa/Mediasite/Play/869e9e5ed1424f18ac2ca76f04709c421d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52 dc.title Hybrid Multilevel Monte Carlo Simulation of Stochastic Reaction Networks dc.type Presentation dc.contributor.department Applied Mathematics and Computational Science Program dc.contributor.department Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division dc.conference.date January 6-9, 2015 dc.conference.name Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015) dc.conference.location KAUST kaust.person Moraes, Alvaro refterms.dateFOA 2018-06-14T03:15:52Z
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