Download PDF by Raymond W. Yeung (auth.): A First Course in Information Theory

By Raymond W. Yeung (auth.)

ISBN-10: 1441986081

ISBN-13: 9781441986085

ISBN-10: 1461346452

ISBN-13: 9781461346456

A First path in info thought is an up to date creation to info conception. as well as the classical subject matters mentioned, it offers the 1st finished therapy of the speculation of I-Measure, community coding thought, Shannon and non-Shannon kind info inequalities, and a relation among entropy and crew thought. ITIP, a software program package deal for proving details inequalities, can also be integrated. With quite a few examples, illustrations, and unique difficulties, this e-book is superb as a textbook or reference ebook for a senior or graduate point path at the topic, in addition to a reference for researchers in similar fields.

Show description

Read or Download A First Course in Information Theory PDF

Best machine theory books

Read e-book online Mathematical Structures for Computer Science: A Modern PDF

New version of the vintage discrete arithmetic textual content for laptop technology majors.

New PDF release: Organizational and Technological Implications of Cognitive

Organizational cognition matters the methods which supply brokers and companies being able to research, make judgements, and remedy difficulties. Organizational and Technological Implications of Cognitive Machines: Designing destiny info administration platforms offers new demanding situations and views to the certainty of the participation of cognitive machines in enterprises.

Download PDF by O Gervasi; MyiLibrary.; et al (eds): Computational science and its applications -- ICCSA 2009 :

The two-volume set LNCS 5592 and 5593 constitutes the refereed court cases of the overseas convention on Computational technology and Its purposes, ICCSA 2009, held in Seoul, Korea, in June/July, 2009. the 2 volumes comprise papers proposing a wealth of unique examine leads to the sphere of computational technology, from foundational concerns in machine technological know-how and arithmetic to complicated functions in almost all sciences employing computational options.

Download PDF by Boris Ryabko, Jaakko Astola, Mikhail Malyutov: Compression-Based Methods of Statistical Analysis and

Common codes successfully compress sequences generated by means of desk bound and ergodic assets with unknown data, and so they have been initially designed for lossless information compression. meanwhile, it used to be discovered that they are often used for fixing very important difficulties of prediction and statistical research of time sequence, and this e-book describes fresh leads to this region.

Additional resources for A First Course in Information Theory

Sample text

However, the existence of the distribution p(Xl , X2 , X3 , X4) constructed immediately after Proposition 2. I2 simply says that it is not always possible to find such a sequence {pd · Therefore, probability distributions which are not strictly positive can be very delicate. 5 that their conditional independence structures are closely related to the factorization problem of such distributions, which has been investigated by Chan and Yeung [43]. 2 SHANNON'S INFORMATION MEASURES We begin this section by introducing the entropy of a random variable.

Let X be a function of Y. Prove that H (X) :S H (Y) . Interpret this result. 12. Prove that for any n ~ 2, n H (X I , X 2," ' , X n ) ~ 2: H(XiIX j , j i= i) . i= 1 13. Prove that Hint: Sum the identities for i = 1,2 , 3 and appl y the result in Problem 12. 14. Let N n = {I , 2, . . , n } and denote H (X i , i E ex) by H (X o. ) for any subset ex of N n . For 1 :S k :S n , let Prove that HI ~ H2 ~ . 39) . See Problem 4 in Chapter 15 for an application of these inequalities. 15. Prove the divergence inequality by using the log-sum inequality.

70) Thus p(x , y, z) logp(z) -+ 0 as p(x , y , z) -+ O. Similarly, we can show that both p(x,y,z)logp(x,z) and p(x ,y,z)logp(y,z) -+ 0 as p(x ,y, z) -+ O. 71) as p(x, y, z) -+ O. Hence, I(X ;YjZ) varies continuously with p even when p(x, y, z) -+ 0 for some x , y, and z . 4 CHAIN RULES In this section, we present a collection of information identities known as the chain rules which are often used in information theory. 23 (CHAIN RULE FOR ENTROPY) n H(X 1,X2 , " ' ,Xn ) = LH(XiIX1 , ,, , ,Xi-d .

Download PDF sample

A First Course in Information Theory by Raymond W. Yeung (auth.)


by Jeff
4.1

Rated 4.93 of 5 – based on 34 votes