Ebook: A First Course in Information Theory
Author: Raymond W. Yeung (auth.)
- Genre: Computers // Algorithms and Data Structures
- Tags: Electrical Engineering, Discrete Mathematics in Computer Science, Probability Theory and Stochastic Processes, Group Theory and Generalizations
- Series: Information Technology: Transmission Processing and Storage
- Year: 2002
- Publisher: Springer US
- Edition: 1
- Language: English
- pdf
A First Course in Information Theory is an up-to-date introduction to information theory. In addition to the classical topics discussed, it provides the first comprehensive treatment of the theory of I-Measure, network coding theory, Shannon and non-Shannon type information inequalities, and a relation between entropy and group theory. ITIP, a software package for proving information inequalities, is also included. With a large number of examples, illustrations, and original problems, this book is excellent as a textbook or reference book for a senior or graduate level course on the subject, as well as a reference for researchers in related fields.
A First Course in Information Theory is an up-to-date introduction to information theory. In addition to the classical topics discussed, it provides the first comprehensive treatment of the theory of I-Measure, network coding theory, Shannon and non-Shannon type information inequalities, and a relation between entropy and group theory.
ITIP, a software package for proving information inequalities, is also included.
With a large number of examples, illustrations, and original problems, this book is excellent as a textbook or reference book for a senior or graduate level course on the subject, as well as a reference for researchers in related fields.
A First Course in Information Theory is an up-to-date introduction to information theory. In addition to the classical topics discussed, it provides the first comprehensive treatment of the theory of I-Measure, network coding theory, Shannon and non-Shannon type information inequalities, and a relation between entropy and group theory.
ITIP, a software package for proving information inequalities, is also included.
With a large number of examples, illustrations, and original problems, this book is excellent as a textbook or reference book for a senior or graduate level course on the subject, as well as a reference for researchers in related fields.
Content:
Front Matter....Pages i-xxiii
The Science of Information....Pages 1-4
Information Measures....Pages 5-39
Zero-Error Data Compression....Pages 41-59
Weak Typicality....Pages 61-71
Strong Typicality....Pages 73-94
The I-Measure....Pages 95-124
Markov Structures....Pages 125-147
Channel Capacity....Pages 149-186
Rate-Distortion Theory....Pages 187-214
The Blahut-Arimoto Algorithms....Pages 215-231
Single-Source Network Coding....Pages 233-262
Information Inequalities....Pages 263-278
Shannon-Type Inequalities....Pages 279-300
Beyond Shannon-Type Inequalities....Pages 301-325
Multi-Source Network Coding....Pages 327-364
Entropy and Groups....Pages 365-387
Back Matter....Pages 389-412
A First Course in Information Theory is an up-to-date introduction to information theory. In addition to the classical topics discussed, it provides the first comprehensive treatment of the theory of I-Measure, network coding theory, Shannon and non-Shannon type information inequalities, and a relation between entropy and group theory.
ITIP, a software package for proving information inequalities, is also included.
With a large number of examples, illustrations, and original problems, this book is excellent as a textbook or reference book for a senior or graduate level course on the subject, as well as a reference for researchers in related fields.
Content:
Front Matter....Pages i-xxiii
The Science of Information....Pages 1-4
Information Measures....Pages 5-39
Zero-Error Data Compression....Pages 41-59
Weak Typicality....Pages 61-71
Strong Typicality....Pages 73-94
The I-Measure....Pages 95-124
Markov Structures....Pages 125-147
Channel Capacity....Pages 149-186
Rate-Distortion Theory....Pages 187-214
The Blahut-Arimoto Algorithms....Pages 215-231
Single-Source Network Coding....Pages 233-262
Information Inequalities....Pages 263-278
Shannon-Type Inequalities....Pages 279-300
Beyond Shannon-Type Inequalities....Pages 301-325
Multi-Source Network Coding....Pages 327-364
Entropy and Groups....Pages 365-387
Back Matter....Pages 389-412
....