An Introduction to Kolmogorov Complexity and Its Applications
Springer International Publishing (Verlag)
978-3-030-11297-4 (ISBN)
This must-read textbook presents an essential introduction to Kolmogorov complexity (KC), a central theory and powerful tool in information science that deals with the quantity of information in individual objects. The text covers both the fundamental concepts and the most important practical applications, supported by a wealth of didactic features.
This thoroughly revised and enhanced fourth edition includes new and updated material on, amongst other topics, the Miller-Yu theorem, the Gács-Kucera theorem, the Day-Gács theorem, increasing randomness, short lists computable from an input string containing the incomputable Kolmogorov complexity of the input, the Lovász local lemma, sorting, the algorithmic full Slepian-Wolf theorem for individual strings, multiset normalized information distance and normalized web distance, and conditional universal distribution.
Dr. Paul M.B. Vitanyi is a CWI Fellow at the Netherlands National Research Institute for Mathematics and Computer Science (CWI), and a Professor of Computer Science at the University of Amsterdam. Dr. Ming Li is Canada Research Chair in Bioinformatics and University Professor at the University of Waterloo, ON, Canada.
Preliminaries.- Algorithmic Complexity.- Algorithmic Prefix Complexity.- Algorithmic Probability.- Inductive Reasoning.- The Incompressibility Method.- Resource-Bounded Complexity.- Physics, Information, and Computation.
Erscheinungsdatum | 21.06.2019 |
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Reihe/Serie | Texts in Computer Science |
Zusatzinfo | XXIII, 834 p. 1 illus. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 178 x 254 mm |
Gewicht | 1790 g |
Themenwelt | Informatik ► Theorie / Studium ► Kryptologie |
Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
Schlagworte | algorithms • Artificial Intelligence • Communication • Complexity • Computer • Computer Science • Information • Information Theory • Intelligence • learning • Learning theory • Logic • Shannon • Statistics • Symbol |
ISBN-10 | 3-030-11297-7 / 3030112977 |
ISBN-13 | 978-3-030-11297-4 / 9783030112974 |
Zustand | Neuware |
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