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Description
The first comprehensive introduction to information theory, this book places the work begun by Shannon and continued by McMillan, Feinstein, and Khinchin on a rigorous mathematical basis. For the first time, mathematicians, statisticians, physicists, cyberneticists, and communications engineers are offered a lucid, comprehensive introduction to this rapidly growing field.
In his first paper, Dr. Khinchin develops the concept of entropy in probability theory as a measure of uncertainty of a finite "scheme," and discusses a simple application to coding theory. The second paper investigates the restrictions previously placed on the study of sources, channels, and codes and attempts "to give a complete, detailed proof of both ... Shannon theorems, assuming any ergodic source and any stationary channel with a finite memory."
Partial Contents: I. The Entropy Concept in Probability Theory -- Entropy of Finite Schemes. The Uniqueness Theorem. Entropy of Markov chains. Application to Coding Theory. II. On the Fundamental Theorems of Information Theory -- Two generalizations of Shannon's inequality. Three inequalities of Feinstein. Concept of a source. Stationarity. Entropy. Ergodic sources. The E property. The martingale concept. Noise. Anticipation and memory. Connection of the channel to the source. Feinstein's Fundamental Lemma. Coding. The first Shannon theorem. The second Shannon theorem.
Author: Alexander I. Khinchin, Aleksandr Iakovlevich Khinchin, A. I. Khinchin
Publisher: Dover Publications
Published: 06/01/1957
Pages: 128
Binding Type: Paperback
Weight: 0.32lbs
Size: 7.86h x 5.74w x 0.30d
ISBN13: 9780486604343
ISBN10: 0486604349
BISAC Categories:
- Science | Philosophy & Social Aspects
- Mathematics | General
In his first paper, Dr. Khinchin develops the concept of entropy in probability theory as a measure of uncertainty of a finite "scheme," and discusses a simple application to coding theory. The second paper investigates the restrictions previously placed on the study of sources, channels, and codes and attempts "to give a complete, detailed proof of both ... Shannon theorems, assuming any ergodic source and any stationary channel with a finite memory."
Partial Contents: I. The Entropy Concept in Probability Theory -- Entropy of Finite Schemes. The Uniqueness Theorem. Entropy of Markov chains. Application to Coding Theory. II. On the Fundamental Theorems of Information Theory -- Two generalizations of Shannon's inequality. Three inequalities of Feinstein. Concept of a source. Stationarity. Entropy. Ergodic sources. The E property. The martingale concept. Noise. Anticipation and memory. Connection of the channel to the source. Feinstein's Fundamental Lemma. Coding. The first Shannon theorem. The second Shannon theorem.
Author: Alexander I. Khinchin, Aleksandr Iakovlevich Khinchin, A. I. Khinchin
Publisher: Dover Publications
Published: 06/01/1957
Pages: 128
Binding Type: Paperback
Weight: 0.32lbs
Size: 7.86h x 5.74w x 0.30d
ISBN13: 9780486604343
ISBN10: 0486604349
BISAC Categories:
- Science | Philosophy & Social Aspects
- Mathematics | General
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