Handbook of Differential Entropy

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A01=Frank Bucholtz
A01=Jonathan M. Nichols
A01=Joseph Victor Michalowicz
advanced entropy estimation techniques
Age Group_Uncategorized
Age Group_Uncategorized
applications of differential entropy
Author_Frank Bucholtz
Author_Jonathan M. Nichols
Author_Joseph Victor Michalowicz
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Ax ?2
Ax Σ2
Beta Distribution
Category1=Non-Fiction
Category=PBT
Category=UB
Category=UY
Cauchy Distribution
communications theory
Continuous Random Variable
COP=United States
Delivery_Delivery within 10-20 working days
density
derivations of differential entropies
Differential Entropy
differential entropy and probability distributions
discrete
discrete and differential entropy
Discrete Entropy
distribution
dynamical systems coupling
entropy as a function of variance
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eq_computing
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estimate radar pulse delays
estimators of parametric and nonparametric differential entropy
function
gaussian
Generalized Beta Distribution
Generalized Normal Distribution
information theory applications
Joint CDF
Joint Entropy
Joint PDF
Kumaraswamy Distribution
Language_English
Log Logistic Distribution
mass
measures of coupling between dynamical system components
measuring the ultimate data compression possible
mixed
Mixed Gaussian Distribution
Mutual Information
Nakagami Distribution
nonparametric entropy estimation
Norm Al
PA=Available
Pareto Distribution
Price_€100 and above
probability
Probability Density Functions
probability density modeling
probability theory and entropy
PS=Active
radar signal analysis
random
Random Process
Random Variables
Rth Moment
softlaunch
statistical signal processing
Support Set
Te
variable
Α2 Erf

Product details

  • ISBN 9781466583160
  • Weight: 492g
  • Dimensions: 156 x 234mm
  • Publication Date: 14 Nov 2013
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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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.

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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