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Statements

Subject Item
dbr:Ensemble_Kalman_filter
rdf:type
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rdfs:label
Ensemble Kalman filter アンサンブルカルマンフィルタ Filtre de Kalman d'ensemble
rdfs:comment
Le filtre de Kalman d'ensemble (EnKF) est une variante du filtre de Kalman plus adaptée pour les problèmes de très grande dimension comme les modèles géophysiques, il a fait son apparition en 1994 dans un article de Geir Evensen. L'idée du filtre de Kalman d'ensemble est de représenter la loi recherchée par un échantillon de la variable d'état, et par suite la matrice de covariance du filtre de Kalman devient une matrice de covariance échantillonnée. Le filtre de Kalman d'ensemble est lié au filtre particulaire (les particules représentent la même chose que les membres de l'ensemble). アンサンブルカルマンフィルタ（Ensemble Kalman Filter；EnKF）とは、逐次型データ同化手法の一つである。シミュレーションモデル内の状態を表す確率変数について、その分布を実現値集合（アンサンブルと称す）によって保持し、観測を得るごとに、観測モデルをもとにしたカルマンフィルターによる推定により、2次モーメントまでが一致するよう、アンサンブルを修正することを繰り返す方法である。 The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component of ensemble forecasting. EnKF is related to the particle filter (in this context, a particle is the same thing as an ensemble member) but the EnKF makes the assumption that all probability distributions involved are Gaussian; when it is applicable, it is much more efficient than the particle filter.
dcterms:subject
dbc:Nonlinear_filters dbc:Monte_Carlo_methods dbc:Linear_filters dbc:Signal_estimation dbc:Bayesian_statistics
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dbo:abstract
The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component of ensemble forecasting. EnKF is related to the particle filter (in this context, a particle is the same thing as an ensemble member) but the EnKF makes the assumption that all probability distributions involved are Gaussian; when it is applicable, it is much more efficient than the particle filter. Le filtre de Kalman d'ensemble (EnKF) est une variante du filtre de Kalman plus adaptée pour les problèmes de très grande dimension comme les modèles géophysiques, il a fait son apparition en 1994 dans un article de Geir Evensen. L'idée du filtre de Kalman d'ensemble est de représenter la loi recherchée par un échantillon de la variable d'état, et par suite la matrice de covariance du filtre de Kalman devient une matrice de covariance échantillonnée. Le filtre de Kalman d'ensemble est lié au filtre particulaire (les particules représentent la même chose que les membres de l'ensemble). * Portail des probabilités et de la statistique アンサンブルカルマンフィルタ（Ensemble Kalman Filter；EnKF）とは、逐次型データ同化手法の一つである。シミュレーションモデル内の状態を表す確率変数について、その分布を実現値集合（アンサンブルと称す）によって保持し、観測を得るごとに、観測モデルをもとにしたカルマンフィルターによる推定により、2次モーメントまでが一致するよう、アンサンブルを修正することを繰り返す方法である。
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dbr:Filter
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