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160601 ||| eng |
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|a 9783319322537
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|a Ryabko, Boris
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| 245 |
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|a Compression-Based Methods of Statistical Analysis and Prediction of Time Series
|h Elektronische Ressource
|c by Boris Ryabko, Jaakko Astola, Mikhail Malyutov
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| 250 |
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|a 1st ed. 2016
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2016, 2016
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| 300 |
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|a IX, 144 p. 29 illus., 21 illus. in color
|b online resource
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| 505 |
0 |
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|a Statistical Methods Based on Universal Codes -- Applications to Cryptography -- SCOT-Modeling and Nonparametric Testing of Stationary Strings
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| 653 |
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|a Mathematics of Computing
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| 653 |
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|a Computational Linguistics
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| 653 |
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|a Computer science / Mathematics
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| 653 |
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|a Statistics
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| 653 |
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|a Data Structures and Information Theory
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| 653 |
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|a Computational linguistics
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| 653 |
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|a Information theory
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| 653 |
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|a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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| 653 |
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|a Data structures (Computer science)
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| 653 |
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|a Natural Language Processing (NLP)
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| 653 |
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|a Natural language processing (Computer science)
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| 700 |
1 |
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|a Astola, Jaakko
|e [author]
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| 700 |
1 |
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|a Malyutov, Mikhail
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
5 |
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|a 10.1007/978-3-319-32253-7
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-319-32253-7?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 003.54
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| 082 |
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|a 005.73
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| 520 |
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|a Universal codes efficiently compress sequences generated by stationary and ergodic sources with unknown statistics, and they were originally designed for lossless data compression. In the meantime, it was realized that they can be used for solving important problems of prediction and statistical analysis of time series, and this book describes recent results in this area. The first chapter introduces and describes the application of universal codes to prediction and the statistical analysis of time series; the second chapter describes applications of selected statistical methods to cryptography, including attacks on block ciphers; and the third chapter describes a homogeneity test used to determine authorship of literary texts. The book will be useful for researchers and advanced students in information theory, mathematical statistics, time-series analysis, and cryptography. It is assumed that the reader has some grounding in statistics and in information theory
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