Abstract
In this paper, a general class of stochastic estimation and control problems is formulated from the Bayesian Decision-Theoretic viewpoint. A discussion as to how these problems can be solved step by step in principle and practice from this approach is presented. As a specific example, the closed form Wiener-Kalman solution for linear estimation in Gaussian noise is derived. The purpose of the paper is to show that the Bayesian approach provides; 1) a general unifying framework within which to pursue further researches in stochastic estimation and control problems, and 2) the necessary computations and difficulties that must be overcome for these problems. An example of a nonlinear, non-Gaussian estimation problem is also solved.
Keywords
Kalman filterBayesian probabilityMathematical optimizationStochastic controlComputer scienceBayes estimatorNonlinear systemLinear-quadratic-Gaussian controlGaussianComputationEstimationMathematicsOptimal controlAlgorithmArtificial intelligenceEngineering
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Publication Info
- Year
- 1964
- Type
- article
- Volume
- 9
- Issue
- 4
- Pages
- 333-339
- Citations
- 620
- Access
- Closed
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Cite This
Y. C. Ho,
R. Lee
(1964).
A Bayesian approach to problems in stochastic estimation and control.
IEEE Transactions on Automatic Control
, 9
(4)
, 333-339.
https://doi.org/10.1109/tac.1964.1105763
Identifiers
- DOI
- 10.1109/tac.1964.1105763