Robert M. Gray's Entropy and Information Theory PDF

By Robert M. Gray

This booklet is an up-to-date model of the knowledge conception vintage, first released in 1990. approximately one-third of the ebook is dedicated to Shannon resource and channel coding theorems; the rest addresses assets, channels, and codes and on details and distortion measures and their homes.

New during this edition:

  • Expanded therapy of desk bound or sliding-block codes and their family to conventional block codes
  • Expanded dialogue of effects from ergodic concept suitable to details theory
  • Expanded remedy of B-processes -- procedures shaped by means of desk bound coding memoryless sources
  • New fabric on buying and selling off details and distortion, together with the Marton inequality
  • New fabric at the houses of optimum and asymptotically optimum resource codes
  • New fabric at the relationships of resource coding and rate-constrained simulation or modeling of random processes

Significant fabric no longer coated in different details concept texts comprises stationary/sliding-block codes, a geometrical view of data concept supplied via technique distance measures, and common Shannon coding theorems for asymptotic suggest desk bound assets, that could be neither ergodic nor desk bound, and d-bar non-stop channels.

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Example text

4. 4 to show that entropy ratesareaffine functions of the underlying probability measures. 2) and = limsup- J 1 - dm(z)-ln m(Xn(z)) = Hm(X). 4) and henee the entropy rate of a stationary diserete alphabet random proeess is an affine funetion of the proeess distribution. 0 Comment: Eq. 4 applied to the random veetors xn stated in terms of the proeess distributions. Eq. 3) states that if we Iook at the Iimit of the normalized log of a mixture of a pair of measures when one ofthe measures governs the proeess, then the Iimit ofthe expeetation does not depend on the other measure at all and is simply the entropy rate of the driving souree.

4 that entropy was a convex function of the underlying distribution. The following Iemma provides similar properties of mutual information considered as a function of either a marginal or a conditional distribution. 4: Let J1. denote a pmf on a discrete space Ax, p(x) Pr(X x), and Iet q be a conditional pmf, q(yjx) Pr(Y yjX x). Let pq denote the resulting joint pmf pq(x,y) p(x)q(yjx). Let IM IM(X;Y) be the average mutual information.

Oo. ) and HPIIM(Q) arefinite and consider the difference We shall show that each of the bracketed terms is nonnegative, which will prove the first inequality. Fix j. lf P( Qj) is 0 we are clone since then also P(Rö) is 0 for all i in the inner sum since these Rö all belong to Qi. If P( Qi) is not 0, we can divide by it to rewrite the bracketed term as where we also used the fact that M( Qi) cannot be 0 since then P( Qi) would also have tobe zero. Since Rö C Qi, P(Rö)/P(Qj) = P(RönQi)/P(Qi) = P(RöiQj) is an elementary conditional probability.

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