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Теория информации / Gray R.M. Entropy and information theory. 1990., 284p

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Entropy and

Information Theory

ii

Entropy and

Information Theory

Robert M. Gray

Information Systems Laboratory

Electrical Engineering Department

Stanford University

Springer-Verlag

New York

iv

This book was prepared with LATEX and reproduced by Springer-Verlag from camera-ready copy supplied by the author.

°c 1990 by Springer Verlag

v

to Tim, Lori, Julia, Peter, Gus, Amy Elizabeth, and Alice

and in memory of Tino

vi

Contents

Prologue

xi

1

Information Sources

1

 

1.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

 

1.2

Probability Spaces and Random Variables . . . . . . . . . . . . .

1

 

1.3

Random Processes and Dynamical Systems . . . . . . . . . . . .

5

 

1.4

Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

 

1.5

Standard Alphabets . . . . . . . . . . . . . . . . . . . . . . . . .

10

 

1.6

Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

 

1.7

Asymptotic Mean Stationarity . . . . . . . . . . . . . . . . . . .

14

 

1.8

Ergodic Properties . . . . . . . . . . . . . . . . . . . . . . . . . .

15

2

Entropy and Information

17

 

2.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17

 

2.2

Entropy and Entropy Rate . . . . . . . . . . . . . . . . . . . . .

17

 

2.3

Basic Properties of Entropy . . . . . . . . . . . . . . . . . . . . .

20

 

2.4

Entropy Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31

 

2.5

Conditional Entropy and Information . . . . . . . . . . . . . . .

35

 

2.6

Entropy Rate Revisited . . . . . . . . . . . . . . . . . . . . . . .

41

 

2.7

Relative Entropy Densities . . . . . . . . . . . . . . . . . . . . . .

44

3 The Entropy Ergodic Theorem

47

 

3.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47

 

3.2

Stationary Ergodic Sources . . . . . . . . . . . . . . . . . . . . .

50

 

3.3

Stationary Nonergodic Sources . . . . . . . . . . . . . . . . . . .

56

 

3.4

AMS Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59

 

3.5

The Asymptotic Equipartition Property . . . . . . . . . . . . . .

63

4

Information Rates I

65

 

4.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65

 

4.2

Stationary Codes and Approximation . . . . . . . . . . . . . . .

65

 

4.3

Information Rate of Finite Alphabet Processes . . . . . . . . . .

73

vii

viii

 

 

CONTENTS

5

Relative Entropy

77

 

5.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . 77

 

5.2

Divergence . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . 77

 

5.3

Conditional Relative Entropy . . . . . . . . . . . . . . .

. . . . . 92

 

5.4

Limiting Entropy Densities . . . . . . . . . . . . . . . .

. . . . . 104

 

5.5

Information for General Alphabets . . . . . . . . . . . .

. . . . . 106

 

5.6

Some Convergence Results . . . . . . . . . . . . . . . . .

. . . . . 116

6

Information Rates II

119

 

6.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . 119

 

6.2

Information Rates for General Alphabets . . . . . . . .

. . . . . 119

 

6.3

A Mean Ergodic Theorem for Densities . . . . . . . . .

. . . . . 122

 

6.4

Information Rates of Stationary Processes . . . . . . . .

. . . . . 124

7

Relative Entropy Rates

131

 

7.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . 131

 

7.2

Relative Entropy Densities and Rates . . . . . . . . . .

. . . . . 131

 

7.3

Markov Dominating Measures . . . . . . . . . . . . . . .

. . . . . 134

 

7.4

Stationary Processes . . . . . . . . . . . . . . . . . . . .

. . . . . 137

 

7.5

Mean Ergodic Theorems . . . . . . . . . . . . . . . . . .

. . . . . 140

8 Ergodic Theorems for Densities

145

 

8.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . 145

 

8.2

Stationary Ergodic Sources . . . . . . . . . . . . . . . .

. . . . . 145

 

8.3

Stationary Nonergodic Sources . . . . . . . . . . . . . .

. . . . . 150

 

8.4

AMS Sources . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . 153

8.5Ergodic Theorems for Information Densities. . . . . . . . . . . . 156

9

Channels and Codes

159

 

9.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

159

 

9.2

Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

160

 

9.3

Stationarity Properties of Channels . . . . . . . . . . . . . . . . .

162

 

9.4

Examples of Channels . . . . . . . . . . . . . . . . . . . . . . . .

165

 

9.5

The Rohlin-Kakutani Theorem . . . . . . . . . . . . . . . . . . .

185

10

Distortion

191

 

10.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

191

 

10.2

Distortion and Fidelity Criteria . . . . . . . . . . . . . . . . . . .

191

 

10.3

Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

193

 

10.4

The rho-bar distortion . . . . . . . . . . . . . . . . . . . . . . . .

195

 

10.5

d-bar Continuous Channels . . . . . . . . . . . . . . . . . . . . .

197

 

10.6

The Distortion-Rate Function . . . . . . . . . . . . . . . . . . . .

201

CONTENTS

ix

11 Source Coding Theorems

211

11.1

Source Coding and Channel Coding . . . . . . . . . . . . . . . .

211

11.2

Block Source Codes for AMS Sources . . . . . . . . . . . . . . . .

211

11.3

Block Coding Stationary Sources . . . . . . . . . . . . . . . . . .

221

11.4

Block Coding AMS Ergodic Sources . . . . . . . . . . . . . . . .

222

11.5

Subadditive Fidelity Criteria . . . . . . . . . . . . . . . . . . . .

228

11.6

Asynchronous Block Codes . . . . . . . . . . . . . . . . . . . . .

230

11.7

Sliding Block Source Codes . . . . . . . . . . . . . . . . . . . . .

232

11.8

A Geometric Interpretation of OPTA’s . . . . . . . . . . . . . . .

241

12 Coding for noisy channels

243

12.1

Noisy Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . .

243

12.2

Feinstein’s Lemma . . . . . . . . . . . . . . . . . . . . . . . . . .

244

12.3

Feinstein’s Theorem . . . . . . . . . . . . . . . . . . . . . . . . .

247

12.4

Channel Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . .

249

12.5

Robust Block Codes . . . . . . . . . . . . . . . . . . . . . . . . .

254

12.6

Block Coding Theorems for Noisy Channels . . . . . . . . . . . .

257

12.7

Joint Source and Channel Block Codes . . . . . . . . . . . . . . .

258

12.8

Synchronizing Block Channel Codes . . . . . . . . . . . . . . . .

261

12.9

Sliding Block Source and Channel Coding . . . . . . . . . . . . .

265

Bibliography

275

Index

 

284

x

CONTENTS