Appendix B:Overview in Probability and Random Processes, B.1 Probability space, B.2 Random variables and random processes

週次課程內容課程影音課程下載
Overview: The philosophy behind information theory線上觀看MP4下載
Chapter 1:Introduction線上觀看MP4下載
Appendix A:Overview on Suprema and Limits
A.1 Supremum and maximum
A.2 Infimum and minimum
A.3 Boundedness and suprema operations
A.4 Sequences and their limits
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Appendix A:Overview on Suprema and Limits
Review A.1-A.4
A.5 Equivalence
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Appendix B:Overview in Probability and Random Processes
B.1 Probability space
B.2 Random variables and random processes
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Appendix B:Overview in Probability and Random Processes
B.3 Statistical properties of random sources
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Appendix B:Overview in Probability and Random
Processes
B.5 Ergodicity and law of large numbers
B.6 Central limit theorem
B.7 Convexity, concavity and Jensen’s inequality
B.8 Lagrange multipliers tech. & KKT conditions
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Chapter 2:Information Measures for Discrete Systems
2.1.1 Self-information
2.1.2 Entropy
2.1.3 Properties of entropy
2.1.4 Joint entropy and conditional entropy
2.1.5 Properties of joint and conditional entropy
2.2 Mutual information
2.2.1 Properties of m
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Chapter 2:Information Measures for Discrete Systems
2.4 Data processing inequality
2.5 Fano’s inequality
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Chapter 2:Information Measures for Discrete Systems
2.6 Divergence and variational distance
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Chapter 2:Information Measures for Discrete System
2.7 Convexity/concavity of information measures
2.8 Fundamentals of hypothesis testing
2.9 R´enyi’s information measures
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Chapter 3:Lossless Data Compression
3.1 Principles of data compression
3.2.1 Block codes for DMS
3.2.2 Block Codes for Stationary Ergodic Sources
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Chapter 3:Lossless Data Compression
3.3 Variable-Length Code for Lossless Data Comp.
3.3.1 Non-singular Codes and Uniquely Decodable Codes
3.3.2 Prefix or Instantaneous Codes
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Chapter 3:Lossless Data Compression
3.3.3 Examples of Binary Prefix Codes
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Chapter 3:Lossless Data Compression
3.3.4 Universal Lossless Variable-Length Codes
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Chapter 4:Data Transmission and Channel Capacity
4.3 Block codes for data transmission over DMCs(1/3)
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Chapter 4:Data Transmission and Channel Capacity
4.3 Block codes for data transmission over DMCs(2/3)
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Chapter 4:Data Transmission and Channel Capacity
4.3 Block codes for data transmission over DMCs(3/3)
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Chapter 4:Data Transmission and Channel Capacity
4.5 Calculating channel capacity
4.5.1 Symmetric, Weakly Symmetric, and Quasi-symmetric Channels
4.5.2 Karuch-Kuhn-Tucker cond. for chan. capacity
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Chapter 4:Data Transmission and Channel Capacity
4.4 Example of Polar Codes for the BEC
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Chapter 4:Data Transmission and Channel Capacity
4.6 Lossless joint source-channel coding and Shannon’s separation principle
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Chapter 5:Differential Entropy and Gaussian Channels
5.1 Differential entropy
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Chapter 5:Differential Entropy and Gaussian Channels
5.1 Differential entropy
5.2 Joint & cond. diff. entrop., diverg. & mutual info
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Chapter 5:Differential Entropy and Gaussian Channels
5.3 AEP for continuous memoryless sources
5.4 Capacity for discrete memoryless Gaussian chan
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Chapter 5:Differential Entropy and Gaussian Channels
5.5 Capacity of Uncorrelated Parallel Gaussian Chan
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Chapter 5:Differential Entropy and Gaussian Channels
5.6 Capacity of correlated parallel Gaussian channels
5.7 Non-Gaussian discrete-time memoryless channels
5.8 Capacity of band-limited white Gaussian channel
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Chapter 6:Lossy Data Compression and Transmission
6.1.1 Motivation
6.1.2 Distortion measures
6.1.3 Frequently used distortion measures
6.2 Fixed-length lossy data compression
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Chapter 6:Lossy Data Compression and Transmission
6.3 Rate-distortion theorem
AEP for distortion typical set
Shannon’s lossy source coding theorem
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Chapter 6:Lossy Data Compression and Transmission
6.4 Calculation of the rate-distortion function
6.4.2 Rate distortion func / the squared error dist
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Chapter 6:Lossy Data Compression and Transmission
6.5 Lossy joint source-channel coding theorem
6.6 Shannon limit of communication systems(1/2)
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Chapter 6:Lossy Data Compression and Transmission
6.6 Shannon limit of communication systems(2/2)
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Preface and Introduction
Chapter 1:Generalized Information Measures for Arbitrary Systems with Memory
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Chapter 2:General Data Compression Theorems線上觀看MP4下載
Chapter 3:Measure of Randomness for Stochastic Processes線上觀看MP4下載
Chapter 4:Channel Coding Theorems and Approximations of Output Statistics for Arbitrary Channels線上觀看MP4下載