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Convergence of Binarized Context-tree Weighting for Estimating Distributions of Stationary Sources

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dc.contributor.authorVellambi, Badri
dc.contributor.authorHutter, Marcus
dc.coverage.spatialVail, USA
dc.date.accessioned2024-01-16T23:17:20Z
dc.date.createdJune 17-22 2018
dc.date.issued2018
dc.date.updated2022-10-02T07:16:21Z
dc.description.abstractThis work investigates the convergence rate of learning the stationary distribution of finite-alphabet stationary ergodic sources using a binarized context-tree weighting approach. The binarized context-tree weighting (overline mathbf Cmathbf Tmathbf W) algorithm estimates the stationary distribution of a symbol as a product of conditional distributions of each component bit, which are determined in a sequential manner using the well known binary context-tree weighting method. We establish that overline mathbf Cmathbf Tmathbf W algorithm is a consistent estimator of the stationary distribution, and that the worst-case L- 1 -prediction error between the overline pmb text CTW and frequency estimates using n source symbols each of which when binarized consists of k > 1 bits decays as Θleft(sqrt 2 kfrac log n nright) ·en_AU
dc.description.sponsorshipThis work was supported by the Australian Research Council Discovery Projects DP120100950 and DP15010459en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.isbn978-1-5386-4780-6en_AU
dc.identifier.urihttp://hdl.handle.net/1885/311517
dc.language.isoen_AUen_AU
dc.provenancehttps://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/..."Authors may share or post their accepted article in the following locations: Author’s employer’s website or institutional repository" from the publisher site (as at 18 Jan 2024). © 2018 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.publisherIEEEen_AU
dc.relationhttp://purl.org/au-research/grants/arc/DP120100950en_AU
dc.relationhttp://purl.org/au-research/grants/arc/DP150104590en_AU
dc.relation.ispartofseries2018 IEEE International Symposium on Information Theory, ISIT 2018en_AU
dc.rights© 2018 IEEEen_AU
dc.sourceIEEE International Symposium on Information Theory - Proceedingsen_AU
dc.titleConvergence of Binarized Context-tree Weighting for Estimating Distributions of Stationary Sourcesen_AU
dc.typeConference paperen_AU
dcterms.accessRightsOpen Access
local.bibliographicCitation.lastpage735en_AU
local.bibliographicCitation.startpage731en_AU
local.contributor.affiliationVellambi Ravisankar, Badri Narayanan, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationHutter, Marcus, College of Engineering and Computer Science, ANUen_AU
local.contributor.authoruidVellambi Ravisankar, Badri Narayanan, u1038607en_AU
local.contributor.authoruidHutter, Marcus, u4350841en_AU
local.description.notesImported from ARIESen_AU
local.description.refereedYes
local.identifier.absfor461301 - Coding, information theory and compressionen_AU
local.identifier.ariespublicationa383154xPUB10646en_AU
local.identifier.doi10.1109/ISIT.2018.8437737en_AU
local.identifier.scopusID2-s2.0-85052486632
local.publisher.urlhttps://www.ieee.org/en_AU
local.type.statusAccepted Versionen_AU

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