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On the foundations of universal sequence prediction

Hutter, Marcus

Description

Solomonoff completed the Bayesian framework by providing a rigorous, unique, formal, and universal choice for the model class and the prior. We discuss in breadth how and in which sense universal (non-i.i.d.) sequence prediction solves various (philosophical) problems of traditional Bayesian sequence prediction. We show that Solomonoff’s model possesses many desirable properties: Fast convergence and strong bounds, and in contrast to most classical continuous prior densities has no zero...[Show more]

dc.contributor.authorHutter, Marcus
dc.date.accessioned2015-08-31T02:20:05Z
dc.date.available2015-08-31T02:20:05Z
dc.identifier.isbn978-3-540-34021-8
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/1885/15031
dc.description.abstractSolomonoff completed the Bayesian framework by providing a rigorous, unique, formal, and universal choice for the model class and the prior. We discuss in breadth how and in which sense universal (non-i.i.d.) sequence prediction solves various (philosophical) problems of traditional Bayesian sequence prediction. We show that Solomonoff’s model possesses many desirable properties: Fast convergence and strong bounds, and in contrast to most classical continuous prior densities has no zero p(oste)rior problem, i.e. can confirm universal hypotheses, is reparametrization and regrouping invariant, and avoids the old-evidence and updating problem. It even performs well (actually better) in non-computable environments.
dc.publisherSpringer Verlag
dc.relation.ispartofTheory and Applications of Models of Computation: Third International Conference, TAMC 2006, Beijing, China, May 15-20, 2006. Proceedings
dc.rights© Springer-Verlag Berlin Heidelberg 2006. http://www.sherpa.ac.uk/romeo/issn/0302-9743/..."Author's post-print on any open access repository after 12 months after publication" from SHERPA/RoMEO site (as at 31/08/15).
dc.subjectSequence prediction
dc.subjectBayes
dc.subjectSolomonoff prior
dc.subjectKolmogorov complexity
dc.subjectOccam's razor
dc.subjectprediction bounds
dc.subjectmodel classes
dc.titleOn the foundations of universal sequence prediction
dc.typeConference paper
local.identifier.citationvolume3959
dc.date.issued2006-05
local.publisher.urlhttp://link.springer.com/
local.type.statusAccepted Version
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National University
local.bibliographicCitation.startpage408
local.bibliographicCitation.lastpage420
local.identifier.doi10.1007/11750321_39
CollectionsANU Research Publications

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