Solomonoff Prediction and Occam's Razor.
Algorithmic information theory gives an idealized notion of compressibility that is often presented as an objective measure of simplicity. It is suggested at times that Solomonoff prediction, or algorithmic information theory in a predictive setting, can deliver an argument to justify Occam's razor....
| Publicado en: | Philosophy of Science Vol. 83; no. 4; pp. 459 - 480 |
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| Formato: | Artículo |
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Cambridge University Press
Oct2016
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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=117944139&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 117944139 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00318248 PSC jtl: Philosophy of Science issn: 00318248 maglogo: N pubinfo: dt: Oct2016 vid: 83 iid: 4 pid: 15979 pub: Cambridge University Press artinfo: ui: 117944139 10.1086/687257 ppf: 459 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 841KB tig: atl: Solomonoff Prediction and Occam's Razor. aug: au: Sterkenburg, Tom F. su: Compressibility Simplicity (Philosophy) Parsimonious models Bayesian analysis Philosophy sug: subj: Compressibility Simplicity (Philosophy) Parsimonious models Bayesian analysis Philosophy ab: Algorithmic information theory gives an idealized notion of compressibility that is often presented as an objective measure of simplicity. It is suggested at times that Solomonoff prediction, or algorithmic information theory in a predictive setting, can deliver an argument to justify Occam's razor. This article explicates the relevant argument and, by converting it into a Bayesian framework, reveals why it has no such justificatory force. The supposed simplicity concept is better perceived as a specific inductive assumption, the assumption of effectiveness. It is this assumption that is the characterizing element of Solomonoff prediction and wherein its philosophical interest lies. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Philosophy of Science is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Philosophy of Science holder: Cambridge University Press dt: @attributes: year: 2016 holdings: @attributes: islocal: N |
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