Contents

 

1. What is Information?        

1.1 Introduction

1.2 Information, Eyes and Evolution

1.3 Finding a Route, Bit By Bit

1.4 A Million Answers to Twenty Questions

1.5 Information, Bits and Binary Digits

1.6 Example 1: Telegraphy

1.7 Example 2: Binary Images

1.8 Example 3: Grey-Level Pictures

1.9 Summary


2. Entropy of Discrete Variables   

2.1 Introduction

2.2 Ground Rules and Terminology

2.3 Shannon's Desiderata

2.4 Information, Surprise and Entropy

2.5 Evaluating Entropy

2.6 Properties of Entropy

2.7 Independent and Identically Distributed Values

2.8 Bits, Shannons, and Bans

2.9 Summary

 

3. The Source Coding Theorem    

3.1 Introduction

3.2 Capacity of a Discrete Noiseless Channel

3.3 Shannon’s Source Coding Theorem 

3.4 Calculating Information Rates

3.5 Data Compression

3.6 The Entropy of English Text

3.7 Why the Theorem is True 

3.8 Kolmogorov Complexity

3.7 Summary

 

4. The Noisy Channel Coding Theorem     

4.1 Introduction 

4.2 Joint Distributions

4.3 Mutual Information

4.4 Conditional Entropy

4.5 Noise and Cross-Talk

4.6 Noisy Pictures and Coding Efficiency 

4.7 Error Correcting Codes

4.8 Capacity of a Noisy Channel

4.9 Shannon’s Noisy Channel Coding Theorem

4.10 Why the Theorem is True

4.11 Summary


5. Entropy of Continuous Variables 

5.1 Introduction   

5.2 The Trouble With Entropy 

5.3 Differential Entropy

5.4 Under-Estimating Entropy

5.5 Properties of Differential Entropy

5.6 Maximum Entropy Distributions

5.7 Making Sense of Differential Entropy

5.8 What is Half a Bit of Information?

5.9 Summary

 

6. Mutual Information: Continuous

6.1 Introduction       

6.2 Joint Distributions

6.3 Conditional Distributions and Entropy

6.4 Mutual Information and Conditional Entropy

6.5 Mutual Information is Invariant

6.6 Kullback-Leibler Divergence

6.7 Summary

 

7. Channel Capacity: Continuous

7.1 Introduction       

7.2 Channel Capacity

7.3 The Gaussian Channel

7.4 Error Rates of Noisy Channels

7.5 Using a Gaussian Channel

7.6 Mutual Information and Correlation

7.7 The Fixed Range Channel

7.8 Summary

 

8. Thermodynamic Entropy and Information

8.1 Introduction

8.2 Physics, Entropy and Disorder

8.3 Information and Thermodynamic Entropy

8.4. Ensembles, Macrostates and Microstates 

8.5 Pricing Information: The Landauer Limit

8.6 The Second Law of Thermodynamics

8.7 Maxwell’s Demon

8.8 Quantum Computation

8.9 Summary


9. Information As Nature’s Currency 

9.1 Introduction      

9.2 Satellite TVs, MP3 and All That

9.3 Does Sex Accelerate Evolution?

9.4 The Human Genome: How Much Information?

9.5 Are Brains Good At Processing Information?

9.6 A Short History of Information Theory

9.7 Summary


Further Reading

 

Appendices  

A. Glossary      

B. Mathematical Symbols     

C. Logarithms            

D. Probability Density Functions

E. Averages From Distributions

F. The Rules of Probability 

G. The Gaussian Distribution          

H. Key Equations


References

Index

 


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