A primer on information theory, with applications to neuroscience
by Felix Effenberger
Publisher: arXiv 2013
Number of pages: 58
This chapter is supposed to give a short introduction to the fundamentals of information theory; not only, but especially suited for people having a less firm background in mathematics and probability theory. Regarding applications, the focus will be on neuroscientific topics.
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by John Daugman - University of Cambridge
The aims of this course are to introduce the principles and applications of information theory. The course will study how information is measured in terms of probability and entropy, and the relationships among conditional and joint entropies; etc.
by Frederic Barbaresco, Ali Mohammad-Djafari - MDPI AG
The aim of this book is to provide an overview of current work addressing topics of research that explore the geometric structures of information and entropy. This survey will motivate readers to explore the emerging domain of Science of Information.
by Robert M. Gray - Information Systems Laboratory
The conditional rate-distortion function has proved useful in source coding problems involving the possession of side information. This book represents an early work on conditional rate distortion functions and related theory.
by Inder Jeet Taneja - Universidade Federal de Santa Catarina
Contents: Shannon's Entropy; Information and Divergence Measures; Entropy-Type Measures; Generalized Information and Divergence Measures; M-Dimensional Divergence Measures and Their Generalizations; Unified (r,s)-Multivariate Entropies; etc.