Bayesian Spectrum Analysis and Parameter Estimation

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

This work is essentially an extensive revision of my Ph.D. dissertation, [1J. It 1S primarily a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis; consequently, we have included a great deal of introductory and tutorial material. Any person with the equivalent of the mathematics background required for the graduateĀ­ level study of physics should be able to follow the material contained in this book, though not without eIfort. From the time the dissertation was written until now (approximately one year) our understanding of the parameter estimation problem has changed extensively. We have tried to incorporate what we have learned into this book. I am indebted to a number of people who have aided me in preparing this docuĀ­ ment: Dr. C. Ray Smith, Steve Finney, Juana Sunchez, Matthew Self, and Dr. Pat Gibbons who acted as readers and editors. In addition, I must extend my deepest thanks to Dr. Joseph Ackerman for his support during the time this manuscript was being prepared.

Author(s): G. Larry Bretthorst (auth.)
Series: Lecture Notes in Statistics 48
Edition: 1
Publisher: Springer-Verlag New York
Year: 1988

Language: English
Pages: 209
City: New York
Tags: Statistics, general

Front Matter....Pages I-XII
Introduction....Pages 1-11
Single Stationary Sinusoid Plus Noise....Pages 13-30
The General Model Equation Plus Noise....Pages 31-41
Estimating the Parameters....Pages 43-53
Model Selection....Pages 55-67
Spectral Estimation....Pages 69-115
Applications....Pages 117-177
Summary and Conclusions....Pages 179-181
Back Matter....Pages 183-209