rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Compression-Based Methods of Statistical Analysis and Prediction of Time Series
  name:
    name part: Ryabko, Boris.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Astola, Jaakko.
    role:
      role term: author.
  name:
    name part: Malyutov, Mikhail.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: IX, 144 p. 29 illus., 21 illus. in color. online resource.
  abstract: 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.
  table of contents: Statistical Methods Based on Universal Codes -- Applications to Cryptography -- SCOT-Modeling and Nonparametric Testing of Stationary Strings.
  note: by Boris Ryabko, Jaakko Astola, Mikhail Malyutov.
  subject:
    topic: Computer science
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Computer science
    topic: Mathematics
  subject:
    topic: Computational linguistics
  subject:
    topic: Statistics
  subject:
    topic: Computer Science
  subject:
    topic: Data Structures, Cryptology and Information Theory
  subject:
    topic: Mathematics of Computing
  subject:
    topic: Language Translation and Linguistics
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  subject:
    topic: Computational Linguistics
  classification: QA76.9.D35
  classification: 005.74
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319322537
  identifier: http://dx.doi.org/10.1007/978-3-319-32253-7
  record info:
    record creation date: 160519
    record change date: 20160525131829.0
    record identifier: 978-3-319-32253-7