Around Kolmogorov Complexity: Basic Notions and Results
by Alexander Shen
Publisher: arXiv.org 2015
Number of pages: 51
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.
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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.
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