Handbook of Differential Entropy

The textbook for a first course in information theory introduces differential entropy as a measure for the limit on data compression. It begins with a brief review of probability theory, which provides an understanding of the core building block of entropy. Then it introduces both discrete and differential entropy and discusses challenges associated with interpreting and deriving the latter for various probability distributions. Final chapters survey several applications where differential entropy has proven useful. Annotation c2014 Ringgold, Inc., Portland, OR (protoview.com)

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.