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dc.contributor.authorLaw, Kody
dc.contributor.authorTembine, Hamidou
dc.contributor.authorTempone, Raul
dc.date.accessioned2017-06-05T08:35:47Z
dc.date.available2017-06-05T08:35:47Z
dc.date.issued2015-01-07
dc.identifier.urihttp://hdl.handle.net/10754/624066
dc.description.abstractA proof of convergence of the standard EnKF generalized to non-Gaussian state space models is provided. A density-based deterministic approximation of the mean-field limiting EnKF (MFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for d < 2 . The fidelity of approximation of the true distribution is also established using an extension of total variation metric to random measures. This is limited by a Gaussian bias term arising from non-linearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.
dc.titleMean-field Ensemble Kalman Filter
dc.typePoster
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)
dc.conference.dateJanuary 6-9, 2015
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
dc.conference.locationKAUST
dc.contributor.institutionNYU Abu Dhabi
kaust.personLaw, Kody
kaust.personTempone, Raul
refterms.dateFOA2018-06-14T03:20:30Z


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