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Estimation theory

Author: Steven M Kay
Publisher: Upper Saddle River, NJ Prentice Hall 1993
Series: Prentice Hall signal processing series
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:

Intended for practicing engineers and scientists who design and analyze signal processing systems. This work offers a unified presentation of parameter estimation for those involved in the design and  Read more...

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Document Type: Book
All Authors / Contributors: Steven M Kay
ISBN: 0133457117 9780133457117
OCLC Number: 177055890
Description: XII, 595 S. Ill., graph. Darst.
Contents: 1. Introduction. 2. Minimum Variance Unbiased Estimation. 3. Cramer-Rao Lower Bound. 4. Linear Models. 5. General Minimum Variance Unbiased Estimation. 6. Best Linear Unbiased Estimators. 7. Maximum Likelihood Estimation. 8. Least Squares. 9. Method of Moments. 10. The Bayesian Philosophy. 11. General Bayesian Estimators. 12. Linear Bayesian Estimators. 13. Kalman Filters. 14. Summary of Estimators. 15. Extension for Complex Data and Parameters. Appendix: Review of Important Concepts. Glossary of Symbols and Abbreviations.
Series Title: Prentice Hall signal processing series
Responsibility: Steven M. Kay

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