What is recorded here?
Recast linear estimation in a state-space formulation with recursively updated estimates and error covariance, supporting nonstationary as well as stationary problems.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The optimality statement concerns the specified linear-estimation problem and its assumptions. It is not unconditional optimal nonlinear Bayesian inference for every non-Gaussian system. Earlier Wiener filtering and the paper’s acknowledged predecessors remain distinct.
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People and systems
R. E. Kalman
Listed source contributors; individual labor shares are not measured
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Documented countries / regions: United States
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English catalogue narrative
Kalman: recursive linear filtering and prediction
Recast linear estimation in a state-space formulation with recursively updated estimates and error covariance, supporting nonstationary as well as stationary problems.
Scientific scope
The optimality statement concerns the specified linear-estimation problem and its assumptions. It is not unconditional optimal nonlinear Bayesian inference for every non-Gaussian system. Earlier Wiener filtering and the paper’s acknowledged predecessors remain distinct.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
R. E. Kalman
Listed source contributors; individual labor shares are not measured