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...
| Publicado en: | Studies in History & Philosophy of Science Part A Vol. 36; no. 2; pp. 391 - 403 |
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| Autores principales: | , , |
| Formato: | Editorial |
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Elsevier B.V.
Jun2005
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=17952930&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 17952930 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00393681 HPS jtl: Studies in History & Philosophy of Science Part A issn: 00393681 maglogo: N pubinfo: dt: Jun2005 vid: 36 iid: 2 pid: 2410 pub: Elsevier B.V. artinfo: ui: 17952930 10.1016/j.shpsa.2005.04.004 ppf: 391 ppct: 12 formats: tig: atl: Empirical data sets are algorithmically compressible: reply to McAllister? aug: au: 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 refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2005 holdings: @attributes: islocal: N |
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