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Shannon paper
Shannon paper
Appendices
Appendices
Appendix 1: Channel Capacity with State Constraints
Appendix 2: Maximum Entropy Derivations
Appendix 3: Ergodic Theorems and AEP
Appendix 4: Proof of the Noisy Channel Coding Theorem
Appendix 5: Function Spaces and Measure Theory
Appendix 6: Continuous Entropy Properties
Part I
Part I
1. The Discrete Noiseless Channel
2. The Discrete Source of Information
3. The Series of Approximations to English
4. Graphical Representation of a Markoff Process
5. Ergodic and Mixed Sources
6. Choice, Uncertainty and Entropy
7. The Entropy of an Information Source
8. Representation of the Encoding and Decoding Operations
9. The Fundamental Theorem for a Noiseless Channel
10. Discussion and Examples
Part II
Part II
11. Representation of a Noisy Discrete Channel
12. Equivocation and Channel Capacity
13. The Fundamental Theorem for a Discrete Channel with Noise
14. Discussion
15. Example of a Discrete Channel and Its Capacity
16. The Channel Capacity in Certain Special Cases
17. An Example of Efficient Coding
Part III
Part III
18. Sets and Ensembles of Functions
19. Band Limited Ensembles of Functions
20. Entropy of a Continuous Distribution
21. Entropy of an Ensemble of Functions
22. Entropy Loss in Linear Filters
23. Entropy of a Sum of Two Ensembles
Part IV
Part IV
24. The Capacity of a Continuous Channel
25. Channel Capacity with an Average Power Limitation
26. The Channel Capacity with a Peak Power Limitation
Part V
Part V
27. Fidelity Evaluation Functions
28. The Rate for a Source Relative to a Fidelity Evaluation
29. The Calculation of Rates
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