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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
Information Theory for Data Communications and Processing
The PDF covers the following topics related to Information Theory : Information Theory for Data Communications and Processing, On the Information Bottleneck Problems: Models, Connections,Applications and Information Theoretic Views, Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding, Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding, Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling, Robust Baseband Compression Against Congestion in Packet-Based Fronthaul Networks Using Multiple Description Coding, Amplitude Constrained MIMO Channels: Properties of Optimal Input Distributions and Bounds on the Capacity, Quasi-Concavity for Gaussian Multicast Relay Channels, Gaussian Multiple Access Channels with One-Bit Quantizer at the Receiver, Efficient Algorithms for Coded Multicasting in Heterogeneous Caching Networks, Cross-Entropy Method for Content Placement and User Association in Cache-Enabled Coordinated Ultra-Dense Networks, Symmetry, Outer Bounds, and Code Constructions: A Computer-Aided Investigation on the Fundamental Limits of Caching.
Author(s): Shlomo Shamai, Abdellatif Zaidi
296 Pages
Information Theory and its applications in theory of computation
This note covers the following topics: Entropy, Kraft's inequality, Source coding theorem, conditional entropy, mutual information, KL-divergence and connections, KL-divergence and Chernoff bounds, Data processing and Fano's inequalities, Asymptotic Equipartition Property, Universal source coding: Lempel-Ziv algorithm and proof of its optimality, Source coding via typical sets and universality, joint typicality and joint AEP, discrete channels and channel capacity, Proof of Noisy channel coding theorem, Constructing capacity-achieving codes via concatenation, Polarization, Arikan's recursive construction of a polarizing invertible transformation, Polar codes construction, Bregman's theorem, Shearer's Lemma and applications, Source coding and Graph entropy, Monotone formula lower bounds via graph entropy, Optimal set Disjointness lower bound and applications, Compression of arbitrary communication protocols, Parallel repetition of 2-prover 1-round games.
Author(s): Venkatesan Guruswami and Mahdi Cheraghchi
NA Pages
Information Theory Lecture Notes
This is a graduate-level introduction to mathematics of information theory. This note will cover both classical and modern topics, including information entropy, lossless data compression, binary hypothesis testing, channel coding, and lossy data compression.
Author(s): Prof. Yury Polyanski
NA Pages
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.
Author(s): Yao Xie
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