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.
Home page url
Download or read it online for free here:
by Robert M. Gray - Springer
The book covers the theory of probabilistic information measures and application to coding theorems for information sources and noisy channels. This is an up-to-date treatment of traditional information theory emphasizing ergodic theory.
by David Feldman - College of the Atlantic
This e-book is a brief tutorial on information theory, excess entropy and statistical complexity. From the table of contents: Background in Information Theory; Entropy Density and Excess Entropy; Computational Mechanics.
by Gregory. J. Chaitin - Cambridge University Press
The book presents the strongest possible version of Gödel's incompleteness theorem, using an information-theoretic approach based on the size of computer programs. The author tried to present the material in the most direct fashion possible.
by Gregory J. Chaitin - Springer
The final version of a course on algorithmic information theory and the epistemology of mathematics. The book discusses the nature of mathematics in the light of information theory, and sustains the thesis that mathematics is quasi-empirical.