This book is an updated version of the information theory classic, first published in 1990. About one-third of the book is devoted to Shannon source and channel coding theorems; the remainder addresses sources, channels, and codes and on information and distortion measures and their properties. New in this edition: Expanded treatment of stationary or sliding-block codes and their relations to traditional block codes Expanded discussion of results from ergodic theory relevant to information theory Expanded treatment of B-processes -- processes formed by stationary coding memoryless sources New material on trading off information and distortion, including the Marton inequality New material on the properties of optimal and asymptotically optimal source codes New material on the relationships of source coding and rate-constrained simulation or modeling of random processes Significant material not covered in other information theory texts includes stationary/sliding-block codes, a geometric view of information theory provided by process distance measures, and general Shannon coding theorems for asymptotic mean stationary sources, which may be neither ergodic nor stationary, and d-bar continuous channels.
Relatively short but dense in content, this work can be a reference to researchers and graduate students doing investigations in information theory, maximum entropy methods in physics, engineering, and statistics, and to all those with a ...
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 ...
First comprehensive introduction to information theory explores the work of Shannon, McMillan, Feinstein, and Khinchin. Topics include the entropy concept in probability theory, fundamental theorems, and other subjects. 1957 edition.
This is just...entropy, he said, thinking that this explained everything, and he repeated the strange word a few times. 1 ?
... −C given in terms of the Euler constant, C, also called the Euler– Mascheroni constant: C = 0.5772156... (3.61) Kozachenko and Leonenko [166] then extend the estimator in Eqn. 3.58 in that the distances between sorted neighbouring ...
MACKAY , D. J. C. , and NEAL , R. M. ( 1995 ) Good codes based for blind source separation . In ICA : Principles and Practice , ed . by S. Roberts and R. Everson . Cambridge Univ . Press . on very sparse matrices .
Scientific knowledge grows at a phenomenal pace--but few books have had as lasting an impact or played as important a role in our modern world as The Mathematical Theory of Communication, published originally as a paper on communication ...
This book, composed of a collection of papers that have appeared in the Special Issue of the Entropy journal dedicated to “Information Theory for Data Communications and Processing”, reflects, in its eleven chapters, novel contributions ...
Some of the most convincing arguments in this regard are in cluded in Fred Dretske's Know/edge & Flow of Information (The M.LT. Press, Cambridge, Mass., 1981) and in this book by Guy lumarie.
Langdon, G.G., 396 Lapidoth, A., xv lATEX, xvi lattice theory, 124 Lauritzen, S.L., 396 laws of information theory, xiii, 264,321, 325 leaf, 46.46–57 Lebesgue measure, 278, 305 Lee, J.Y.-B., xvi Lee, T.T., 147, 401 Leibler, R.A., 39, ...