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Handbook of Differential Entropy

Handbook of Differential Entropy

One of the main issues in communications theory is measuring the ultimate data compression possible using the concept of entropy. While differential entropy may seem to be a simple extension of the discrete case, it is a more complex measure that often requires a more careful tre... read full description below.

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ISBN 9781138374799
Barcode 9781138374799
Published 18 September 2018 by Taylor & Francis Ltd
Format Paperback
Alternate Format(s) View All (2 other possible title(s) available)
Author(s) By Michalowicz, Joseph Victor
By Nichols, Jonathan M.
By Bucholtz, Frank
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Full details for this title

ISBN-13 9781138374799
ISBN-10 1138374792
Stock Available
Status Showing available at publisher; usually ships 7-14 working days
Publisher Taylor & Francis Ltd
Imprint CRC Press
Publication Date 18 September 2018
Publication Country United Kingdom United Kingdom
Format Paperback
Author(s) By Michalowicz, Joseph Victor
By Nichols, Jonathan M.
By Bucholtz, Frank
Category Probability & Statistics
General Theory of Computing
Number of Pages 244
Dimensions Width: 159mm
Height: 235mm
Weight 449g
Interest Age 19+ years
Reading Age 19+ years
NBS Text Science & Mathematics: Textbooks & Study Guides
ONIX Text College/higher education
Dewey Code 003.54015153
Catalogue Code Not specified

Description of this Book

One of the main issues in communications theory is measuring the ultimate data compression possible using the concept of entropy. While differential entropy may seem to be a simple extension of the discrete case, it is a more complex measure that often requires a more careful treatment. Handbook of Differential Entropy provides a comprehensive introduction to the subject for researchers and students in information theory. Unlike related books, this one brings together background material, derivations, and applications of differential entropy. The handbook first reviews probability theory as it enables an understanding of the core building block of entropy. The authors then carefully explain the concept of entropy, introducing both discrete and differential entropy. They present detailed derivations of differential entropy for numerous probability models and discuss challenges with interpreting and deriving differential entropy. They also show how differential entropy varies as a function of the model variance. Focusing on the application of differential entropy in several areas, the book describes common estimators of parametric and nonparametric differential entropy as well as properties of the estimators. It then uses the estimated differential entropy to estimate radar pulse delays when the corrupting noise source is non-Gaussian and to develop measures of coupling between dynamical system components.

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Author's Bio

Joseph V. Michalowicz is a consultant with Sotera Defense Solutions. He retired from the U.S. Naval Research Laboratory as head of the Sensor and Data Processing Section in the Optical Sciences Division. He has published extensively in the areas of mathematical modeling, probability and statistics, signal detection, multispectral infrared sensors, and category theory. He received a Ph.D. in mathematics with a minor in electrical engineering from the Catholic University of America. Jonathan M. Nichols is a member of the Maritime Sensing Section in the Optical Sciences Division at the U.S. Naval Research Laboratory. His research interests include signal and image processing, parameter estimation, and the modeling and analysis of infrared imaging devices. He received a Ph.D. in mechanical engineering from Duke University. Frank Bucholtz is head of the Advanced Photonics Section at the U.S. Naval Research Laboratory. He has published in the areas of microwave signal processing and microwave photonics, fiber optic sensors, micro-optical devices, nonlinear dynamics and chaos, hyperspectral imaging systems, and information theory. His current research focuses on optical components for digital communications. He received a Ph.D. in physics from Brown University.

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