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Lecture Notes On Information 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.
Author(s): Yihong Wu
342 Pages 
Lecture Notes on Information Theory by Prof. Dr. rer. nat. Rudolf Mathar
This lecture note covers introduction, Fundamentals of Information Theory, Source Coding and Information Channels.
Author(s): Prof. Dr. rer. nat. Rudolf Mathar, Institute for Theoretical Information Technology Kopernikusstr, Germany
59 Pages
Applied Digital Information theory
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.
Author(s): James L.Massey
153 Pages
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.
Author(s): John Duchi
464 Pages
Advanced Information Theory notes
This book contains following contents: Information Theory for Discrete Variables, Information Theory for Continuous Variables, Channel Coding, Typical Sequences and Sets, Lossy Source Coding, Distributed Source Coding, Multiaccess Channels.
Author(s): Prof Dr. sc. techn. Gerhard Kramer
180 Pages
This book explains basics of thermodynamics, including thermodynamic potentials, microcanonical and canonical distributions, and evolution in the phase space, The inevitability of irreversibility, basics of information theory, applications of information theory, new second law of thermodynamics and quantum information.
Author(s): Gregory Falkovich
165 Pages
Information Theory by Y. Polyanskiy
This PDF covers the following topics related to Information Theory : Information measures, Lossless data compression, Binary hypothesis testing, Channel coding, Lossy data compression, Advanced topics.
Author(s): Y. Polyanskiy, Y. Wu
295 PagesAdvertisement
