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.
By F. Bavaud, J. C. Chappelier, and J. Kohlas—This long
note contains a good survey of information theory and its applications. It
introduces the basic ideas of uncertainty and information, then also the more
practical extensions such as optimal coding schemes, followed by the theories
underlying versions of stationary processes and Markov chains. Other challenges,
as the note addresses, pertain to noisy transmission environments in coding.
Highlighted here are several advanced topics that follow, including,
importantly, error-correcting codes and cryptography. The resource will give
both a theoretical background and a practical overview of how to encode,
transmit, and secure information effectively. It is a very important guide for
those who seek a deep understanding of information theory and how it relates to
real problems of communication and data processing.
This
lecture note navigates through information theory, statistics and measure theory. It
covers fundamental concepts such as definitions, chain rules, data processing
inequalities, and divergences and extends to optimal procedures, LeCam’s and
Fano’s inequalities, and operational results like entropy and source coding. It
also focus on exponential families and statistical modeling, fitting procedures,
and lower bounds on testing parameters, sub-Gaussian and sub-exponential random
variables, martingale methods, uniformity covering topics such as
Kullback-Leibler divergence, PAC-Bayes bounds, interactive data analysis, and
error bounds.
Om Carter-Introduction to information theory and entropy: It goes in deep
to do some basic concepts of information theory, focusing on the concept of
entropy and its applications. It does so by first investigating the measure of
complexity and the elementary theories of probability before introducing some
key ideas in information theory. It ranges from basic issues, such as entropy
theory and the Gibbs inequality, up to Shannon's communication theory but also
to practical applications in many diversified fields. Other topics dealt with
are Bayes Theorem, analog channels, the Maximum Entropy Principle, and
applications to biology and physics. The Kullback-Leibler information measure
will be discussed in trying to cast light upon quantification of information and
its relations with different fields of science. This book should be ideal for
the general reader interested in information theory and its immense areas of
application..
The lecture notes Advanced
Information Theory Notes by Prof. Dr. sc. techn. Gerhard Kramer cover advanced
topics in information theory. Information theory within the context of these
notes starts with discrete and continuous random variables to base the student
in deeper understandings of complicated scenarios. The key areas include channel
coding, important for good data transmission; typical sequences and sets, which
are fundamental in the theoretical and practical applications of the coding. The
text also explores lossy source coding and distributed source coding, which look
into how data might be compressed and transmitted with much efficiency. It also
covers multiaccess channels, an important aspect in showing just how different
sources of data interact. Such a broad-ranging textbook seems particularly
suited to readers having a firm grounding in basic information theory, wanting
to advance into more advanced areas as well as applications.
This is a PDF document written by
J.G. Daugman on the fundamentals of the theory of information and coding.
Beginning with the very basic concept of probability and uncertainty, and the
concept of information, it arrives at entropies and their meaning. It deals with
the source coding theorems: prefix, variable-length, and fixed-length codes. It
looks into several kinds of channels, their properties, noise, and channel
capacity. The further topics delve into detail with continuous information,
noisy channel coding theorems, Fourier series elaborated on in making matters of
convergence, orthogonal representation, and useful Fourier theorems. The text
also expands into aspects such as sampling and aliasing, DFT, FFT algorithms,
and the quantized degrees-of-freedom in continuous signals and concludes with
discussions on the Gabor-Heisenberg-Weyl uncertainty relation and Kolmogorov
complexity for a general overview of some of the key principles of information
theory and coding.
This set of lecture notes by Venkatesan Guruswami
and Mahdi Cheraghchi addresses the intersection of information theory and
theoretical computer science. The core topics to be covered in the lecture note
include entropy, Kraft's inequality, source coding theorem, conditional entropy,
and mutual information. It also covers KL-divergence, Chernoff bounds, data
processing, and Fano's inequalities. Key concepts include AEP, universal source
coding using the Lempel-Ziv algorithm, and proof of its optimality. It covers
discrete channels and channel capacity, the Noisy Channel Coding Theorem, and
how to construct capacity-achieving codes by concatenation and by polar codes.
Additional topics: Bregman's theorem, Shearer's Lemma, graph entropy, and
applications to optimal set disjointness lower bounds. This text offers a
wide-ranging view of how the basic principles of information theory shed light
on the construction of algorithms, and the establishment of bounds-on the
complexity of problems in the field of theoretical computation.
Author(s): Venkatesan
Guruswami and Mahdi Cheraghchi