The ensemble Kalman filter (EnKF) has been proposed as a Monte Carlo, derivative-… Kalman
filter, and is now widely used in sequential data assimilation, where state vectors of huge …

… (57)] discusses a method using SVD, with the same asymptotic cost, but requiring the data …
Mandel, A two-stage ensemble Kalman filter for smooth data assimilation. Environmental and …

… Monte Carlo state estimation procedure called the ensemble Kalman filter (EnKF) [4], … large.
This is accomplished by recursively updating an ensemble of samples such that the sample …

… Carlo sampling are studied and compared: the ensemble Kalman filter and the particle filter.
… In this section, the basic theory on ensemble Kalman filters and particle filters are recalled, …

… These algorithms rely on an ensemble of interacting particles … Despite the practical success
of ensemble Kalman filters, … This paper investigates ensemble Kalman filters that incorporate …

… in the ensemble Kalman filter. Collectively, the large sample asymptotics of the substitute
iid … In contrast, the elements in the ensemble Kalman filter are dependent, because they all …

… Our approach is to use an ensemble Kalman filter update to go from π p … large. If the linear
algebra is carefully implemented, the ensemble Kalman particle filter has the same asymptotic …

… the standard ensemble Kalman filter (EnKF) from Le Gland, Monbet, and Tran [Large sample
asymptotics for the ensemble Kalman filter, in The Oxford Handbook of Nonlinear Filtering, …

… Since we are interested in large ensemble asymptotics and the linear case, localization and
… understanding ensemble filters is a law of large numbers as the size of the ensemble grows …

… The ensemble Kalman filter (EnKF) is a Monte Carlo-based implementation of the Kalman
filter (KF) for extremely high-dimensional, possibly … 4.1 Asymptotic convergence results …