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On Sequence Prediction for Arbitrary Measures

Ryabko, Daniil; Hutter, Marcus

Description

Suppose we are given two probability measures on the set of one-way infinite finite-alphabet sequences. Consider the question when one of the measures predicts the other, that is, when conditional probabilities converge (in a certain sense), if one of the measures is chosen to generate the sequence. This question may be considered a refinement of the problem of sequence prediction in its most general formulation: for a given class of probability measures, does there exist a measure which...[Show more]

dc.contributor.authorRyabko, Daniil
dc.contributor.authorHutter, Marcus
dc.date.accessioned2015-08-28T01:25:10Z
dc.date.available2015-08-28T01:25:10Z
dc.identifier.isbn978-1-4244-1397-3
dc.identifier.urihttp://hdl.handle.net/1885/15010
dc.description.abstractSuppose we are given two probability measures on the set of one-way infinite finite-alphabet sequences. Consider the question when one of the measures predicts the other, that is, when conditional probabilities converge (in a certain sense), if one of the measures is chosen to generate the sequence. This question may be considered a refinement of the problem of sequence prediction in its most general formulation: for a given class of probability measures, does there exist a measure which predicts all of the measures in the class? To address this problem, we find some conditions on local absolute continuity which are sufficient for prediction and generalize several different notions that are known to be sufficient for prediction. We also formulate some open questions to outline a direction for finding the conditions on classes of measures for which prediction is possible.
dc.description.sponsorshipSNF grant 200020-107616.
dc.publisherIEEE
dc.relation.ispartofIEEE International Symposium on Information Theory, 2007. ISIT 2007
dc.rightsAuthors are free to post the accepted version of their articles on their personal Web sites or those of their employers. http://www.ieee.org/publications_standards/publications/rights/index.html as at 28/08/2015
dc.rights© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
dc.titleOn Sequence Prediction for Arbitrary Measures
dc.typeConference paper
dc.date.issued2007-06
local.type.statusAccepted Version
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National University
local.bibliographicCitation.startpage2346
local.bibliographicCitation.lastpage2350
local.identifier.doi10.1109/ISIT.2007.4557570
CollectionsANU Research Publications

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