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 intro

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.

Probability in Brief. The Concept of Entropy. Entropy for Discrete Probability Distributions. Differential Entropies for Probability Distributions. Differential Entropy as a Function of Variance. Applications of Differential Entropy. Appendices. Bibliography.