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
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 Lecture Notes
This PDF covers the following topics related to Information Theory, Introduction, Entropy, Relative Entropy, and Mutual Information, Asymptotic Equipartition Properties, Communication and Channel Capacity, Method of Types, Conditional and Joint Typicality, Lossy Compression and Rate Distortion Theory, Joint Source Channel Coding.
Author(s): Prof. Tsachy Weissman
75 Pages
Information Theory and Coding cam
The PDF covers the following topics related to Information Theory, Foundations, probability, uncertainty, information, Entropies defined, and why they are measures of information, Source coding theorem, prefix, variable, and fixedlength codes, Channel types, properties, noise, and channel capacity, Continuous information, density, noisy channel coding theorem, Fourier series, convergence, orthogonal representation, Useful Fourier theorems, transform pairs, Sampling, aliasing, Discrete Fourier transform, Fast Fourier Transform Algorithms, The quantised degreesoffreedom in a continuous signal, GaborHeisenbergWeyl uncertainty relation, Kolmogorov complexity.
Author(s): J G Daugman
75 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
