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Algorithmic probability

Vitanyi, Paul; Legg, Shane; Hutter, Marcus

Description

Algorithmic "Solomonoff" Probability (AP) assigns to objects an a priori probability that is in some sense universal. This prior distribution has theoretical applications in a number of areas, including inductive inference theory and the time complexity analysis of algorithms. Its main drawback is that it is not computable and thus can only be approximated in practice.

dc.contributor.authorVitanyi, Paul
dc.contributor.authorLegg, Shane
dc.contributor.authorHutter, Marcus
dc.date.accessioned2015-08-28T01:29:35Z
dc.date.available2015-08-28T01:29:35Z
dc.identifier.issn1941-6016
dc.identifier.urihttp://hdl.handle.net/1885/15013
dc.description.abstractAlgorithmic "Solomonoff" Probability (AP) assigns to objects an a priori probability that is in some sense universal. This prior distribution has theoretical applications in a number of areas, including inductive inference theory and the time complexity analysis of algorithms. Its main drawback is that it is not computable and thus can only be approximated in practice.
dc.publisherScholarpedia
dc.rightshttp://www.scholarpedia.org/article/Scholarpedia:About..."all published articles contents are available under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License (please see the Terms of Use)" as at 27/08/2015
dc.sourceScholarpedia
dc.subjectalgorithmic information theory
dc.subjectalgorithmic complexity
dc.subjectdiscrete/continuous algorithmic probability
dc.titleAlgorithmic probability
dc.typeJournal article
local.identifier.citationvolume2
dc.date.issued2007
local.publisher.urlhttp://www.scholarpedia.org/
local.type.statusPublished Version
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National University
local.bibliographicCitation.issue8
local.bibliographicCitation.startpage2572
local.identifier.doi10.4249/scholarpedia.2572
CollectionsANU Research Publications

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