Information Theory and Coding
by John Daugman
Publisher: University of Cambridge 2009
Number of pages: 75
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; how these are used to calculate the capacity of a communication channel, with and without noise; coding schemes, including error correcting codes; how discrete channels and measures of information generalize to their continuous forms; etc.
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by Felix Effenberger - arXiv
This chapter is supposed to give a short introduction to the fundamentals of information theory, especially suited for people having a less firm background in mathematics and probability theory. The focus will be on neuroscientific topics.
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We introduce algorithmic information theory, also known as the theory of Kolmogorov complexity. We explain this quantitative approach to defining information and discuss the extent to which Kolmogorov's and Shannon's theory have a common purpose.
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Lecture notes for a graduate course focusing on the relations between Information Theory and Statistical Physics. The course is aimed at EE graduate students in the area of Communications and Information Theory, or graduate students in Physics.
Data compression is useful in some situations because 'compressed data' will save time (in reading and on transmission) and space if compared to the unencoded information it represent. In this book, we describe the decompressor first.