TY - JOUR
T1 - Deterministic Mean-Field Ensemble Kalman Filtering
AU - Law, Kody
AU - Tembine, Hamidou
AU - Tempone, Raul
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the King Abdullah University of Science and Technology (KAUST) SRI-UQ Center.
PY - 2016/5/3
Y1 - 2016/5/3
N2 - The proof of convergence of 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, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. A density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence k between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d
AB - The proof of convergence of 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, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. A density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence k between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d
UR - http://hdl.handle.net/10754/608649
UR - http://epubs.siam.org/doi/10.1137/140984415
UR - http://www.scopus.com/inward/record.url?scp=84976902222&partnerID=8YFLogxK
U2 - 10.1137/140984415
DO - 10.1137/140984415
M3 - Article
SN - 1064-8275
VL - 38
SP - A1251-A1279
JO - SIAM Journal on Scientific Computing
JF - SIAM Journal on Scientific Computing
IS - 3
ER -