By Thomas M. Cover
ISBN-10: 0471241954
ISBN-13: 9780471241959
ISBN-10: 047174882X
ISBN-13: 9780471748823
The most recent variation of this vintage is up to date with new challenge units and material
the second one version of this basic textbook continues the book's culture of transparent, thought-provoking guideline. Readers are supplied once more with an instructive mixture of arithmetic, physics, records, and knowledge theory.
all of the crucial subject matters in details idea are coated intimately, together with entropy, info compression, channel capability, cost distortion, community info idea, and speculation trying out. The authors offer readers with an effective knowing of the underlying conception and functions. challenge units and a telegraphic precis on the finish of every bankruptcy extra support readers. The historic notes that persist with every one bankruptcy recap the most points.
the second one version features:
* Chapters reorganized to enhance teaching
* 2 hundred new problems
* New fabric on resource coding, portfolio concept, and suggestions capacity
* up to date references
Now present and stronger, the second one version of parts of knowledge idea is still the right textbook for upper-level undergraduate and graduate classes in electric engineering, facts, and telecommunications.
An Instructor's handbook featuring certain strategies to all of the difficulties within the publication is out there from the Wiley editorial department.Content:
Chapter 1 creation and Preview (pages 1–12):
Chapter 2 Entropy, Relative Entropy, and Mutual details (pages 13–55):
Chapter three Asymptotic Equipartition estate (pages 57–69):
Chapter four Entropy premiums of a Stochastic technique (pages 71–101):
Chapter five facts Compression (pages 103–158):
Chapter 6 playing and knowledge Compression (pages 159–182):
Chapter 7 Channel capability (pages 183–241):
Chapter eight Differential Entropy (pages 243–259):
Chapter nine Gaussian Channel (pages 261–299):
Chapter 10 cost Distortion idea (pages 301–346):
Chapter eleven info idea and facts (pages 347–408):
Chapter 12 greatest Entropy (pages 409–425):
Chapter thirteen common resource Coding (pages 427–462):
Chapter 14 Kolmogorov Complexity (pages 463–508):
Chapter 15 community details concept (pages 509–611):
Chapter sixteen info conception and Portfolio conception (pages 613–656):
Chapter 17 Inequalities in info conception (pages 657–687):
Read or Download Elements of Information Theory, Second Edition PDF
Similar theory books
Download e-book for iPad: Prediction Theory for Finite Populations by Heleno Bolfarine
A lot of papers have seemed within the final 20 years on estimating and predicting features of finite populations. This monograph is designed to offer this contemporary concept in a scientific and constant demeanour. The authors' strategy is that of superpopulation versions within which values of the inhabitants components are regarded as random variables having joint distributions.
We not construct constructions like we used to nor can we pay for them within the related means. constructions this day are not any longer basically safeguard yet also are existence help structures, communique terminals, facts production facilities, and masses extra. constructions are awfully dear instruments that has to be consistently adjusted to operate successfully.
- Use of Humic Substances to Remediate Polluted Environments: From Theory to Practice: Proceedings of the NATO Advanced Research Workshop on Use of Humates to Remediate Polluted Environments: From Theory to Practice Zvenigorod, Russia 23–29 September 2002
- Rethinking Scientific Change and Theory Comparison: Stabilities, Ruptures, Incommensurabilities?
- Theory and Design Methods of Special Space Orbits
- Foundations of Coding Theory
- Activity Theory in Formal and Informal Science Education
Extra info for Elements of Information Theory, Second Edition
Sample text
Thus, I (X; Y |Z) ≤ I (X; Y ). 122) Thus, the dependence of X and Y is decreased (or remains unchanged) by the observation of a “downstream” random variable Z. Note that it is also possible that I (X; Y |Z) > I (X; Y ) when X, Y , and Z do not form a Markov chain. For example, let X and Y be independent fair binary random variables, and let Z = X + Y . Then I (X; Y ) = 0, but I (X; Y |Z) = H (X|Z) − H (X|Y, Z) = H (X|Z) = P (Z = 1)H (X|Z = 1) = 12 bit. 9 SUFFICIENT STATISTICS This section is a sidelight showing the power of the data-processing inequality in clarifying an important idea in statistics.
Mutual information is a special case of a more general quantity called relative entropy, which is a measure of the distance between two probability distributions. All these quantities are closely related and share a number of simple properties, some of which we derive in this chapter. In later chapters we show how these quantities arise as natural answers to a number of questions in communication, statistics, complexity, and gambling. That will be the ultimate test of the value of these definitions.
Sequence of coin tosses of a coin with unknown parameter θ = Pr(Xi = 1). Given n, the number of 1’s is a sufficient statistic for θ . Here T (X1 , X2 , . . , Xn ) = ni=1 Xi . In fact, we can show that given T , all sequences having that many 1’s are equally likely and independent of the parameter θ . Specifically, n Pr (X1 , X2 , . . , Xn ) = (x1 , x2 , . . , xn ) Xi = k i=1 = 1 (nk) 0 if xi = k, otherwise. 125) Thus, θ → Xi → (X1 , X2 , . . , Xn ) forms a Markov chain, and T is a sufficient statistic for θ .
Elements of Information Theory, Second Edition by Thomas M. Cover
by Kevin
4.1