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dc.contributor.authorHaji Ali, Abdul Lateef
dc.date.accessioned2017-06-08T06:32:30Z
dc.date.available2017-06-08T06:32:30Z
dc.date.issued2016-01-08
dc.identifier.urihttp://hdl.handle.net/10754/624865
dc.description.abstractI discuss using single level and multilevel Monte Carlo methods to compute quantities of interests of a stochastic particle system in the mean-field. In this context, the stochastic particles follow a coupled system of Ito stochastic differential equations (SDEs). Moreover, this stochastic particle system converges to a stochastic mean-field limit as the number of particles tends to infinity. I start by recalling the results of applying different versions of Multilevel Monte Carlo (MLMC) for particle systems, both with respect to time steps and the number of particles and using a partitioning estimator. Next, I expand on these results by proposing the use of our recent Multi-index Monte Carlo method to obtain improved convergence rates.
dc.titleA study of Monte Carlo methods for weak approximations of stochastic particle systems in the mean-field?
dc.typePresentation
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.dateJanuary 5-10, 2016
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
dc.conference.locationKAUST
kaust.personHaji Ali, Abdul Lateef
refterms.dateFOA2018-06-13T14:45:32Z


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