Around Kolmogorov Complexity: Basic Notions and Results
by Alexander Shen
Publisher: arXiv.org 2015
Number of pages: 51
Description:
Algorithmic information theory studies description complexity and randomness and is now a well known field of theoretical computer science and mathematical logic. This report covers the basic notions of algorithmic information theory: Kolmogorov complexity (plain, conditional, prefix), Solomonoff universal a priori probability, notions of randomness, effective Hausdorff dimension.
Download or read it online for free here:
Download link
(300KB, PDF)
Similar books
The Limits of Mathematicsby Gregory J. Chaitin - Springer
The final version of a course on algorithmic information theory and the epistemology of mathematics. The book discusses the nature of mathematics in the light of information theory, and sustains the thesis that mathematics is quasi-empirical.
(13371 views)
Information Theory and Codingby John Daugman - University of Cambridge
The aims of this course are to introduce the principles and applications of information theory. The course will study how information is measured in terms of probability and entropy, and the relationships among conditional and joint entropies; etc.
(24563 views)
Essential Coding Theoryby Venkatesan Guruswami, Atri Rudra, Madhu Sudan - University at Buffalo
Error-correcting codes are clever ways of representing data so that one can recover the original information even if parts of it are corrupted. The basic idea is to introduce redundancy so that the original information can be recovered ...
(9970 views)
Information Theory, Inference, and Learning Algorithmsby David J. C. MacKay - Cambridge University Press
A textbook on information theory, Bayesian inference and learning algorithms, useful for undergraduates and postgraduates students, and as a reference for researchers. Essential reading for students of electrical engineering and computer science.
(30184 views)