This note will explore the basic
concepts of information theory. It is highly recommended for students planning
to delve into the fields of communications, data compression, and statistical
signal processing. Topics covered includes: Entropy and mutual information,
Chain rules and inequalities, Data processing, Fano's inequality, Asymptotic
equipartition property, Entropy rate, Source coding and Kraft inequality,
Optimal code length and roof code, Huffman codes, Shannon-Fano-Elias and
arithmetic codes, Maximum entropy, Channel capacity, Channel coding theorem,
Differential entropy, Gaussian channel, Parallel Gaussian channel and
water-filling, Quantization and rate-distortion.
explains the following topics: Measuring Information, Joint Entropy, Relative
Entropy and Mutual Information, Sources with Memory, Asymptotic Equipartition
Property and Source Coding, Channel Capacity and Coding, Continuous Sources and
Gaussian Channel, Rate Distortion Theory.
This note covers the
following topics: Introduction to Information theory, a simple data compression
problem, transmission of two messages over a noisy channel, measures of
information and their properties, Source and Channel coding, Data compression,
transmission over noisy channels, Differential entropy, Rate-distortion theory.
These notes provide a
graduate-level introduction to the mathematics of Information Theory. Topics covered includes:
Information measures: entropy, divergence and mutual information, Sufficient
statistic, Extremization of mutual information, Lossless data compression,
Channel coding, Linear codes, Lossy data compression, Applications to
statistical decision theory, Multiple-access channel, Entropy method in
combinatorics and geometry.