《连续系统信息论》由日本名古屋大学数学系的教授井原俊辅（Shunsuke Ihara）所著，在数学、连续系统信息论及其应用方面颇有建树，发表了多篇有关论文和著作。
本书系统地以数学方式分析了嫡和随机过程，特别是高斯过程及其在信息理论中的应用。全书内容大致分为两部分：第一部分对嫡在信息论、概率论和数理统计中的统一处理进行了详细的介绍；第二部分主要讨论连续通信系统的信息论，专注于高斯信道及其在实践中的应用。书中各部分都附有相应示例和练习，便于读者理解与检验所学，书末尾还有大量参考文献目录，可为读者进一步学习提供参考。本书的一个鲜明特点是，与大多数强调离散的信息论书籍不同，本书作者强调的是连续的通信系统。

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