Information Theory Inference and Learning Algorithms
This book contains below topics, Introduction to information theory, Probability, entropy, and inference, Source coding, Noisy channel coding, Sparse graph codes, Bayesian inference and Monte Carlo methods, Neural networks, Decision theory.
Author(s): David J. C. MacKay, University of Cambridge
640 Pages
An Introduction to Information Theory and Applications
This note explains the following topics, uncertainty and information, Efficient coding of information, Stationary processes and markov chains, Coding for noisy transmission, Complements to efficient coding of Information, Error correcting codes and cryptography.
Author(s): F Bavaud, J C Chappelier and J Kohlas
293 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 Shannons 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, LeCams and Fanos 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, subGaussian and subexponential random variables, martingale methods, uniformity covering topics such as KullbackLeibler divergence, PACBayes 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 Shannons communication theory, Application to Biology, Examples using Bayes Theorem, Analog channels, A Maximum Entropy Principle, Application to Physics lasers, KullbackLeibler information measure.
Author(s): Tom Carter
139 Pages
Information Theory and its applications in theory of computation
This note covers the following topics, Entropy, Krafts inequality, Source coding theorem, conditional entropy, mutual information, KLdivergence and connections, KLdivergence and Chernoff bounds, Data processing and Fanos inequalities, Asymptotic Equipartition Property, Universal source coding, LempelZiv 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 capacityachieving codes via concatenation, Polarization, Arikans recursive construction of a polarizing invertible transformation, Polar codes construction, Bregmans theorem, Shearers 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 2prover 1round games.
Author(s): Venkatesan Guruswami and Mahdi Cheraghchi
NA Pages
