Information Theory and Statistics - A Tutorial

Information Theory and Statistics: A Tutorial is concerned with applications of information theory concepts in statistics, in the finite alphabet setting. The topics covered include large deviations, hypothesis testing, maximum likelihood estimation in exponential families, analysis of contingency tables, and iterative algorithms with an "information geometry" background. Also, an introduction is provided to the theory of universal coding, and to statistical inference via the minimum description length principle motivated by that theory.

The tutorial does not assume the reader has an in-depth knowledge of Information Theory or statistics. As such, Information Theory and Statistics: A Tutorial, is an excellent introductory text to this highly-important topic in mathematics, computer science and electrical engineering. It provides both students and researchers with an invaluable resource to quickly get up to speed in the field.

List of contents

Preface 1 Preliminaries 2 Large deviations, hypothesis testing 3 I-projections 4 f-Divergence and contingency tables 5 Iterative algorithms 6 Universal coding 7 Redundancy bounds 8 Redundancy and the MDL principle Appendix A Summary of process concepts Historical notes References

Product details

• Authors: Imre Csisz4ar, Imre Csiszar, Paul Shields
• Publisher: Now Publishers Inc
• Languages: English
• Product format: Paperback / Softback
• Released: 15.12.2004
• EAN: 9781933019055
• ISBN: 978-1-933019-05-5
• No. of pages: 124
• Dimensions: 156 mm x 234 mm x 7 mm
• Weight: 202 g
• Series: Foundations and Trends (R) in Communications and Information Theory
• Subject: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering