Empirical data sets are algorithmically compressible: reply to McAllister?

Abstract: James McAllister’s 2003 article, ‘Algorithmic randomness in empirical data’, claims that empirical data sets are algorithmically random, and hence incompressible. We show that this claim is mistaken. We present theoretical arguments and empirical evidence for compressibility, and discuss t...

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Publicado en:Studies in History & Philosophy of Science Part A Vol. 36; no. 2; pp. 391 - 403
Autores principales: Twardy, Charles, Gardner, Steve, Dowe, David L.
Formato: Editorial
Publicado: Elsevier B.V. Jun2005
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jun2005
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        10.1016/j.shpsa.2005.04.004
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        atl: Empirical data sets are algorithmically compressible: reply to McAllister?
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          Twardy, Charles
          Gardner, Steve
          Dowe, David L.
        affil: Computer Science & Software Engineering, Monash University, VIC 3800, Australia
      su:
        Algorithms
        McAllister, James
        Minimum message length (Information theory)
        Algebra
      sug:
        subj:
          Algorithms
          McAllister, James
          Minimum message length (Information theory)
          Algebra
      keyword:
        Algorithmic information theory
        Algorithmic randomness
        Compression
        Empirical data
        Induction
        Information
        Law
        Minimum message length
        MML
        Pattern
      ab: Abstract: James McAllister’s 2003 article, ‘Algorithmic randomness in empirical data’, claims that empirical data sets are algorithmically random, and hence incompressible. We show that this claim is mistaken. We present theoretical arguments and empirical evidence for compressibility, and discuss the matter in the framework of Minimum Message Length (MML) inference.
      pubtype: Academic Journal
      doctype: Editorial
      src: R
    language: English
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