| Titre : | Fundamentals of Statistical Processing Vol. I : estimation Theory | | Type de document : | texte imprimé | | Auteurs : | Steven M. Kay, Auteur | | Editeur : | Upper Saddle River, New Jersey : Pearson/Prentice Hall | | Année de publication : | 1993 | | Collection : | Prentice Hall Signal Processing Series | | Importance : | 595 p. | | Présentation : | couv. ill. en coul., ill. | | Format : | 24 cm. | | ISBN/ISSN/EAN : | 978-0-13-345711-7 | | Langues : | Anglais (eng) | | Catégories : | AUTOMATISME
| | Index. décimale : | 25-02 Théorie et traitement du signal | | Résumé : | A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. KEY TOPICS: Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc. | | Note de contenu : | 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. |
Fundamentals of Statistical Processing Vol. I : estimation Theory [texte imprimé] / Steven M. Kay, Auteur . - Upper Saddle River, New Jersey : Pearson/Prentice Hall, 1993 . - 595 p. : couv. ill. en coul., ill. ; 24 cm.. - ( Prentice Hall Signal Processing Series) . ISBN : 978-0-13-345711-7 Langues : Anglais ( eng) | Catégories : | AUTOMATISME
| | Index. décimale : | 25-02 Théorie et traitement du signal | | Résumé : | A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. KEY TOPICS: Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc. | | Note de contenu : | 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. |
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