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内容简介

　　《信息论基础（英文版）》作者（杨伟豪）现为香港中文大学网络编码研究所主任，是网络编码理论的提出者之一。本书原版自2002年出版以来，被哥伦比亚大学、康奈尔大学、麻省理工学院、斯坦福大学等美国著名学府所采用，是信息理论方面的重要教材。本书首先介绍了信息论的经典内容，然后全面详细地论述了，度量、网络编码、Shannon型与非Shannon型信息不等式等理论，以及熵函数与群论之间的关系。《信息论基础（英文版）》中配有大量的实例、插图和习题，适合作为通信、电子信息、计算机等专业的高年级本科生和研究生的教材，也可供相关领域的科研人员参考。

作者简介

　　杨伟豪（Raymond w．Yeung）教授1988年毕业于美国康奈尔大学。获博士学位。之后曾在AT&T贝尔实验室工作3年，于1 991年加入香港中文大学，现为网络编码研究所主任，是网络编码理论的提出者之一，其主要研究领域为信息理论与网络编码。他还是IEEE Fellow和香港工程师学会会士。

目　　录

1. THE SCIENCE OF INFORMATION
2. INFORMATION MEASURES
2.1 Independence and Markov Chains
2.2 Shannon's Information Measures
2.3 Continuity of Shannon's Information Measures
2.4 Chain Rules
2.5 Informational Divergence
2.6 The Basic Inequalities
2.7 Some Useful Information Inequalities
2.8 Fano's Inequality
2.9 Entropy Rate of Stationary Source
Problems
Historical Notes
3. ZERO-ERROR DATA COMPRESSION

1. THE SCIENCE OF INFORMATION 
2. INFORMATION MEASURES 
 2.1 Independence and Markov Chains 
 2.2 Shannon's Information Measures 
 2.3 Continuity of Shannon's Information Measures 
 2.4 Chain Rules 
 2.5 Informational Divergence 
 2.6 The Basic Inequalities 
 2.7 Some Useful Information Inequalities 
 2.8 Fano's Inequality 
 2.9 Entropy Rate of Stationary Source 
 Problems 
 Historical Notes 
3. ZERO-ERROR DATA COMPRESSION 
 3.1 The Entropy Bound 
 3.2 Prefix Codes 
  3.2.1 Definition and Existence 
  3.2.2 Huffman Codes 
 3.3 Redundancy of Prefix Codes 
 Problems 
 Historical Notes 
4. WEAK TYPICALITY 
 4.1 The Weak AEP 
 4.2 The Source Coding Theorem 
 4.3 Efficient Source Coding 
 4.4 The Shannon-McMiilan-BreimanTheorem 
 Problems 
 Historical Notes 
5. STRONG TYPICALITY 
 5.1 StrongAEP 
 5.2 Strong Typicality Versus Weak Typicality 
 5.3 Joint Typicality 
 5.4 An Interpretation of the Basic Inequalities 
 Problems 
 Historical Notes 
6. THE/-MEASURE 
 6.1 Preliminaries 
 6.2 The/-Measure for Two Random Variables 
 6.3 Construction of the/-Measure ч* 
 6.4 #* Can be Negative 
 6.5 Information Diagrams 
 6.6 Examples of Applications 
 Appendix 6.A: A Variation of the Inclusion-Exclusion Formula 
 Problems 
 Historical Notes 
7. MARKOV STRUCTURES 
 7.1 Conditional Mutual Independence 
 7.2 Full Conditional Mutual Independence 
 7.3 Markov Random Field 
 7.4 Markov Chain 
 Problems 
 Historical Notes 
8. CHANNEL CAPACITY 
 8.1 Discrete MemorylessChannels 
 8.2 The Channel Coding Theorem 
 8.3 The Converse 
 8.4 Achievability of the Channel Capacity 
 8.5 A Discussion 
 8.6 Feedback Capacity 
 8.7 Separation of Source and Channel Coding 
 Problems 
 Historical Notes 
9. RATE-DISTORTION THEORY 
 9.1 Single-Letter Distortion Measures 
 9.2 The Rate-Distortion Function R(D) 
 9.3 The Rate-Distortion Theorem 
 9.4 The Converse 
 9.5 Achievability of RI(D) 
 Problems 
 Historical Notes 
10. THE BLAHUT-ARIMOTO ALGORITHMS 
 10.I Alternating Optimization 
 10.2 The Algorithms 
  10.2.1 Channel Capacity 
  10.2.2 The Rate-Distortion Function 
 10.3 Convergence 
  10.3.1- A Sufficient Condition 
  10.3.2 Convergence to the Channel Capacity 
 Problems 
 Historical Notes 
11. SINGLE-SOURCE NETWORK CODING 
 11.1 A Point-to-Point Network 
 11.2 What is Network Coding? 
 11.3 A Network Code 
 11.4 The Max-Flow Bound 
 11.5 Achievability of the Max-Flow Bound 
  11.5.1 Acyclic Networks 
  11.5.2 Cyclic Networks 
 Problems 
 Historical Notes 
12. INFORMATION INEQUALITIES 
 12.1 The Region Fn 
 12.2 Information Expressions in Canonical Form 
 12.3 A Geometrical Framework 
  12.3.1 Unconstrained Inequalities 
  12.3.2 Constrained Inequalities 
  12.3.3 Constrained Identities 
 12.4 Equivalence of Constrained Inequalities 
 12.5 The Implication Problem of Conditional Independence 
 Problems 
 Historical Notes 
13 SHANNON-TYPE INEQUALITIES 
 13.1 The Elemental Inequalities 
 13.2 A Linear Programming Approach 
  13.2.1 Unconstrained Inequalities 
  13.2.2 Constrained Inequalities and Identities 
 13.3 A Duality 
 13.4 Machine Proving - ITIP 
 13.5 Tackling the Implication Problem 
 13.6 Minimality of the Elemental Inequalities 
 Appendix 13.A: The Basic Inequalities and the Polymatroidal 
  Axioms 
 Problems 
 Historical Notes 
14. BEYOND SHANNON-TYPE INEQUALITIES 
 14.1 Characterizations of г2,г3, and гn 
 14.2 A Non-Shannon-Type Unconstrained Inequality 
 14.3 A Non-Shannon-Type Constrained Inequality 
 14.4 Applications 
 Problems 
 Historical Notes 
15. MULTI-SOURCE NETWORK CODING 
 15.1 Two Characteristics 
  15.1.1 The Max-Flow Bounds 
  15.1.2 Superposition Coding 
 15.2 Examples of Application 
  15.2.1 Multilevel Diversity Coding 
  15.2.2 Satellite Communication Network 
 15.3 A Network Code for Acyclic Networks 
 15.4 An Inner Bound 
 15.5 An Outer Bound 
 15.6 The LP Bound and Its Tightness 
 15.7 Achievability of Rin 
 Appendix 15.A: Approximation of Random Variables with 
  Infinite Alphabets 
 Problems 
 Historical Notes 
16. ENTROPY AND GROUPS 
 16.1 Group Preliminaries 
 16.2 Group-Characterizable Entropy Functions 
 16.3 A Group Characterization of гn 
 16.4 Information Inequalities and Group Inequalities 
 Problems 
 Historical Notes 
Bibliography 
Index

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