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Algorithmic probability

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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.date.issued2007
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.en_AU
dc.identifier.issn1941-6016en_AU
dc.identifier.urihttp://hdl.handle.net/1885/15013
dc.publisherScholarpediaen_AU
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 27/08/2015en_AU
dc.sourceScholarpediaen_AU
dc.subjectalgorithmic information theoryen_AU
dc.subjectalgorithmic complexityen_AU
dc.subjectdiscrete/continuous algorithmic probabilityen_AU
dc.titleAlgorithmic probabilityen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.issue8en_AU
local.bibliographicCitation.startpage2572en_AU
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National Universityen_AU
local.contributor.authoruidu4350841en_AU
local.identifier.citationvolume2en_AU
local.identifier.doi10.4249/scholarpedia.2572en_AU
local.publisher.urlhttp://www.scholarpedia.org/en_AU
local.type.statusPublished Versionen_AU

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