Information Theory in Computer Science

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

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

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

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 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