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Information Theory by Himanshu Tyagi
This note covers the following topics: Introduction to Information theory, a simple data compression problem, transmission of two messages over a noisy channel, measures of information and their properties, Source and Channel coding, Data compression, transmission over noisy channels, Differential entropy, Rate-distortion theory.
Author(s): Himanshu Tyagi
NA 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
An introduction to information Theory and Entropy
This note covers Measuring complexity, Some probability ideas, Basics of information theory, Some entropy theory, The Gibbs inequality, A simple physical example Shannon’s communication theory, Application to Biology, Examples using Bayes Theorem, Analog channels, A Maximum Entropy Principle, Application to Physics(lasers), Kullback-Leibler information measure.
Author(s): Tom Carter
139 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 in Computer Science
This note explains the following topics: Shearer's Lemma, Entropy, Relative Entropy, Hypothesis testing, total variation distance and Pinsker's lemma, Stability in Shearer's Lemma, Communication Complexity, Set Disjointness, Direct Sum in Communication Complexity and Internal Information Complexity, Data Structure Lower Bounds via Communication Complexity, Algorithmic Lovasz Local Lemma, Parallel Repetition Theorem, Graph Entropy and Sorting.
Author(s): Madhu Sudan
NA Pages
Information Theory, Inference, and Learning Algorithms (David J.C. MacKay)
Currently this section contains no detailed description for the page, will update this page soon.
Author(s):
Pages
A Short Course in Information Theory (D. MacKay)
Currently this section contains no detailed description for the page, will update this page soon.
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