Pages that link to "Item:Q5963729"
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The following pages link to An efficient implementation of the ensemble Kalman filter based on an iterative Sherman-Morrison formula (Q5963729):
Displaying 17 items.
- Adaptive error covariances estimation methods for ensemble Kalman filters (Q350039) (← links)
- Ensemble Kalman filtering with shrinkage regression techniques (Q536583) (← links)
- Improved initial sampling for the ensemble Kalman filter (Q732177) (← links)
- A generalized polynomial chaos based ensemble Kalman filter with high accuracy (Q834092) (← links)
- Computing the pseudoinverse of specific Toeplitz matrices using rank-one updates (Q1793737) (← links)
- Local search methods for the solution of implicit inverse problems (Q1800277) (← links)
- A reduced-space line-search method for unconstrained optimization via random descent directions (Q2007776) (← links)
- An efficient ensemble Kalman filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge (Q2027172) (← links)
- Score matching filters for Gaussian Markov random fields with a linear model of the precision matrix (Q2072672) (← links)
- An ensemble Kalman filter implementation based on the Ledoit and Wolf covariance matrix estimator (Q2222066) (← links)
- Non-linear data assimilation via trust region optimization (Q2322777) (← links)
- Variational ensemble Kalman filtering using limited memory BFGS (Q2447382) (← links)
- An ensemble forecast method based on observational errors (Q3193389) (← links)
- An Ensemble Kalman Filter Implementation Based on Modified Cholesky Decomposition for Inverse Covariance Matrix Estimation (Q4610145) (← links)
- Randomized tensor decomposition for large-scale data assimilation problems for carbon dioxide sequestration (Q6084299) (← links)
- An adjoint-free four-dimensional variational data assimilation method via a modified Cholesky decomposition and an iterative Woodbury matrix formula (Q6166395) (← links)
- A sparse matrix formulation of model-based ensemble Kalman filter (Q6172920) (← links)
