Information Theory and its applications in theory of computation
Information Theory and its applications in theory of computation
Information Theory and its applications in theory of computation
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
The lecture notes
of Prof. Dr. rer. nat. Rudolf Mathar give a clear and very compact introduction
into information theory. These notes are divided into three key parts: the
basics of information theory, source coding, and information channels. The
introduction treats the basic notions and definitions in information theory in a
very solid way. Source coding gives methods and different techniques that are
used in encoding information, while the information channels section discusses
the pattern in which information is carried and noise that affects it. This
resource is a good pick for students and professionals who seek structure in the
principles of information theory and its applications from a respected expert in
the field.
Author(s): Prof. Dr. rer. nat. Rudolf Mathar, Institute
for Theoretical Information Technology Kopernikusstr, Germany
This note serves as a comprehensive guide to fundamental concepts in
information theory and coding. This pdf provides discrete probability theory,
information theory, and coding principles. Beginning with Shannon's measure of
information, then delves into the efficient coding of information, the
methodology of typical sequences is introduced, emphasizing the distinction
between lossy and lossless source encoding. The text also discusses coding for
noisy digital channels, block coding principles and tree and trellis coding
principles.
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.
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.
Prof. Tsachy Weissman's
lecture notes are an excellent summary of the core topics in the subject of
information theory. The document initiates with a basic overview of entropy and
relative entropy, followed by mutual information and asymptotic equipartition
property. Further, it discusses communications theory, channel capacity, and the
method of types. It also covers key topics such as typicality-conditioned and
joint, lossy compression, and rate-distortion theory. The notes also include
joint source-channel coding, where there is quite a good grasp of the principles
and applications of information theory. These notes will be very helpful for
students and professionals looking forward to structured, comprehensive
knowledge about the subject.
This
is a wide-ranging text by Shlomo Shamai and Abdellatif Zaidi, covering both
foundational and advanced topics in information theory applied to data
communications and processing. It discusses basic issues, such as information
bottleneck problems, unsupervised clustering via methods of the variational
information bottleneck, and rate-distortion analysis. It proceeds to get into
subjects of a higher level of difficulty: non-orthogonal eMBB and URLLC radio
access, robust baseband compression techniques, and amplitude-constrained MIMO
channels. Efficient algorithms have been derived for multicasting, content
placement in cache networks, and the fundamental limits of caching. The title
will be a ready reference for researchers and practitioners interested in the
theory and practice of modern communication systems, comprehensively covering
recent advancement efforts and applications in information theory.