Preface Chapter 1.Entropy 1.1 Information Transmission 1.2 Entropy 1.3 Entropy for Continuous Distributions 1.4 Relative Entropy 1.5 Properties of Relative Entropy 1.6 Mutual Information 1.7 Rate-Distortion Function 1.8 Entropy for Gaussian Distributions Historical Notes Chapter 2.Stochastic Processes and Entropy 2.1 Entropy Rate 2.2 Discrete Time Stationary Processes 2.3 Discrete Time Markov Stationary Processes 2.4 Entropy Rate of a Stationary Gaussian Process 2.5 Continuous Time Stationary Processes 2.6 Band Limited Processes 2.7 Discrete Time Observation of Continuous Time Processes Historical Notes Chapter 3.Maximum Entropy Analysis 3.1 Maximum Entropy and Minimum Relative Entropy 3.2 Large Deviation Theorems 3.3 Maximum Entropy Spectral Analysis 3.4 Hypothesis Testing 3.5 Hypothesis Testing for Stationary Gaussian Processes Historical Notes Chapter 4.Theory of Information Transmission 4.1 Model of Communication Systems 4.2 Information Stability 4.3 Source Coding Theorems 4.4 Channel Capacity 4.5 Channel Coding Theorems 4.6 Fundamental Theorem in Information Transmission Historical Notes Chapter 5.Discrete Time Gaussian Channels 5.1 Mutual Information in Channels with Additive Noise 5.2 Discrete Gaussian Channels 5.3 Mutual Information in Gaussian Systems 5.4 Capacity of Discrete White Gaussian Channels 5.5 Capacity of Discrete Gaussian Channels without Feedback 5.6 Optimal Codings in Discrete White Gaussian Channels 5.7 Capacity of Discrete Gaussian Channels with Feedback 5.8 Rate-Distortion Function of a Discrete Gaussian Message 5.9 Coding Theorem for Discrete Gaussian Channels Historical Notes Chapter 6.Continuous Time Gaussian Channels 6.1 Continuous Gaussian Channels 6.2 Mutual Information in White Gaussian Channels (I) 6.3 Mutual Information in White Gaussian Channels (II) 6.4 Capacity of White Gaussian Channels 6.5 Optimal Codings in White Gaussian Channels 6.6 Gaussian Channels without Feedback 6.7 Stationary Gaussian Channels 6.8 Gaussian Channels with Feedback
6.9 Rate-Distortion Function of a Gaussian Process 6.10 Coding Theorem for Gaussian Channels Historical Notes Appendix.Preparation from Probability Theory A.1 Probability and Random Variable A.2 Conditional Probability and Conditional Expectation A.3 Stochastic Integral Bibliography List of Symbols Subject Index
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