Information Theory and its applications in theory of computation
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Information Theory and its applications in theory of computation
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
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.
This book explains basics of thermodynamics, including thermodynamic
potentials, microcanonical and canonical distributions, and evolution in the
phase space, The inevitability of irreversibility, basics of information theory,
applications of information theory, new second law of thermodynamics and quantum
information.
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 & Rate Distortion Theory, Joint Source Channel
Coding.
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.
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 fixed-length 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 degrees-of-freedom
in a continuous signal, Gabor-Heisenberg-Weyl uncertainty relation, Kolmogorov
complexity.
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.