A First Course in Information Theory (Hardcover)
Raymond W. Yeung
- 出版商: Harcourt
- 出版日期: 2006-07-03
- 售價: $1,050
- 貴賓價: 9.5 折 $998
- 語言: 英文
- 頁數: 412
- 裝訂: Hardcover
- ISBN: 0306467917
- ISBN-13: 9780306467912
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商品描述
Incepted half a century ago, information theory is a
classical yet modern field which is more vibrant than ever before. In
particular, there have been a number of major research results on the foundation
of the theory during the last ten years. These results enable information theory
to be understood and explored in a way which has not been possible before, and
they open new dimensions in the theory. In short, the depth of information
theory is far beyond what we used to know.
This book is an integration
of the most fundamental topics in information theory plus a few selected
advanced topics. All concepts and technicalities are explained with clarity.
Except for a few classical results, all the results included here are not found
elsewhere in book form. These include the theory of I-Measure, Shannon-type and
non-Shannon-type information inequalities, and network coding theory. Some
important implications of information theory in probability theory and group
theory are also explained in this book.
ITIP, the software package that
comes with the book, is the only software package of its kind which can prove
all Shannon-type information inequalities. It is an essential tool for all
information theorists.
This book is suitable for use as a textbook, or
as a reference book with any other textbook in a course on information theory.
It is also an essential reference for researchers working in areas related to
this subject matter.
`No one since Shannon has had a better
appreciation for the mathematical structure of information quantities
than Prof. Yeung. ... Yeung unveils a smørgasbord of topics in modern
information theory that heretofore have been available only in research
papers.'
Toby Berger, Cornell University
Contents
1. The Science of Information. 2. Information Measures. 3. Zero-Error Data Compression. 4. Weak Typicality. 5. Strong Typicality. 6. The I-Measure. 7. Markov Structures. 8. Channel Capacity. 9. Rate Distortion Theory. 10. The Blahut-Arimoto Algorithms. 11. Single-Source Network Coding. 12. Information Inequalities. 13. Shannon-Type Inequalities. Appendix 13A: The Basic Inequalities and the Polymatroidal Axioms. 14. Beyond Shannon-Type Inequalities. 15. Multi-Source Network Coding. Appendix 15A: Approximation of Random Variables with Infinite Alphabets. 16. Entropy and Groups. Bibliography. Index.