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An Introduction to Single-User Information Theory

An Introduction to Single-User Information Theory

Hardcover

Series: Springer Undergraduate Texts in Mathematics and Technology

General ComputersGeneral Mathematics

PREORDER - Expected ship date October 28, 2026

ISBN10: 981921548X
ISBN13: 9789819215485
Publisher: Springer
Published: Oct 28 2026
Pages: 534
Language: English

This second edition is extensively updated to reflect the growing synergy between information theory and artificial intelligence (AI). Key enhancements include:

  • New Content Focused on AI & Machine Learning: A major new chapter introduces generalized information and statistical measures (such as parameterized Rényi, Jensen, and f-divergence), alongside expanded coverage of the information bottleneck principle, cross-entropies, and generalized information inequalities, all connected to modern ML applications like deep learning and generative models.
  • Comprehensive Updates Throughout: All existing chapters have been revised and expanded, the appendix enhanced, and chapter problem sets significantly enlarged based on the first edition. The book presents a succinct and mathematically rigorous treatment of the main pillars of Shannon's information theory, discussing the fundamental concepts and indispensable results of Shannon's mathematical theory of communications. It includes six meticulously written core chapters (with accompanying problems), emphasizing the key topics of information measures (classical and generalized); lossless and lossy data compression; channel coding; and joint source-channel coding for single-user (point-to-point) communications systems. It also features two appendices covering necessary background material in real analysis and in probability theory and stochastic processes.
  • Improved Accuracy & Resources: Errors from the first edition have been corrected, and the bibliography and index are thoroughly updated. A separate, 400-page instructor's solutions manual is also available.

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General Mathematics