The Science of Information -- The Science of Information -- Fundamentals of Network Coding -- Information Measures -- Information Measures -- Zero-Error Data Compression -- Weak Typicality -- Strong Typicality -- Discrete Memoryless Channels -- Rate-Distortion Theory -- The Blahut–Arimoto Algorithms -- Differential Entropy -- Continuous-Valued Channels -- Markov Structures -- Information Inequalities -- Shannon-Type Inequalities -- Beyond Shannon-Type Inequalities -- Entropy and Groups -- Fundamentals of Network Coding -- The Max-Flow Bound -- Single-Source Linear Network Coding: Acyclic Networks -- Single-Source Linear Network Coding: Cyclic Networks -- Multi-source Network Coding.

Information Theory and Network Coding consists of two parts: Components of Information Theory, and Fundamentals of Network Coding Theory. Part I is a rigorous treatment of information theory for discrete and continuous systems. In addition to the classical topics, there are such modern topics as the I-Measure, Shannon-type and non-Shannon-type information inequalities, and a fundamental relation between entropy and group theory. With information theory as the foundation, Part II is a comprehensive treatment of network coding theory with detailed discussions on linear network codes, convolutional network codes, and multi-source network coding. Other important features include: Derivations that are from the first principle A large number of examples throughout the book Many original exercise problems Easy-to-use chapter summaries Two parts that can be used separately or together for a comprehensive course Information Theory and Network Coding is for senior undergraduate and graduate students in electrical engineering, computer science, and applied mathematics. This work can also be used as a reference for professional engineers in the area of communications.