Fundamentals of Statistical Processing,

A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms.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.

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.

What is included with this book?

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

FREE SHIPPING

Rent More, Save More! Use code: BBRENTAL

5% off 1 book, 7% off 2 books, 10% off 3+ books

Fundamentals of Statistical Processing, Volume I Estimation Theory

Summary

Table of Contents

Supplemental Materials