Lecture Notes on statistics and information Theory
Lecture Notes on statistics and information Theory
Lecture Notes on statistics and information Theory
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.
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.
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
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.
It serves as a basis for
everything, from the very basics of thermodynamics and information theory to
thermodynamic potentials and distributions, principles of irreversibility, phase
space evolution, and beyond. The book informs the readers about the very basics
of information theory: basic notions, basic definitions, and applications. It
also offers a fresh perspective on the second law of thermodynamics and quantum
information, and insights into the modern view of how information theory is
intertwined with the laws of physics. This book will be very useful to anyone
who wants to gain an understanding of the basic issues in both thermodynamics
and information theory and their intersection in current usage.
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.