Elements of Information Theory

Elements of Information Theory

1991 • 784 pages

Ratings1

Average rating4

15

The latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points. The Second Edition features: Chapters reorganized to improve teaching 200 new problems New material on source coding, portfolio theory, and feedback capacity Updated references Now current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Become a Librarian

Reviews

Popular Reviews

Reviews with the most likes.

There are no reviews for this book. Add yours and it'll show up right here!


Top Lists

See all (2)

List

32 books

Logos

Against Method
Why We Get Sick
Climbing Mount Improbable
No bullshit guide to math and physics
Thinking physics
The Order of Time
How to Teach Quantum Physics to Your Dog

List

51 books

Textbooks

Neural Networks and Deep Learning
The Feynman Lectures on Physics
The Theoretical Minimum: What You Need to Know to Start Doing Physics
Prospect theory
Quantum Computation and Quantum Information
An Introduction to Statistical Learning
The Elements of Statistical Learning: Data Mining, Inference, and Prediction