Recursive Variational Inference for Total Least-Squares

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dc.contributor"European Union (EU)" & "Horizon 2020"
dc.contributor.authorFriml, Dominikcs
dc.contributor.authorVáclavek, Pavelcs
dc.coverage.issue1cs
dc.coverage.volume7cs
dc.date.accessioned2024-01-19T20:38:29Z
dc.date.available2024-01-19T20:38:29Z
dc.date.issued2023-06-26cs
dc.description.abstractThis letter analyzes methods for deriving credible intervals to facilitate errors-in-variables identification by expanding on Bayesian total least squares. The credible intervals are approximated employing Laplace and variational approximations of the intractable posterior density function. Three recursive identification algorithms providing an approximation of the credible intervals for inference with the Bingham and the Gaussian priors are proposed. The introduced algorithms are evaluated on numerical experiments, and a practical example of application on battery cell total capacity estimation compared to the state-of-the-art algorithms is presented.en
dc.formattextcs
dc.format.extent2839-2844cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE Control Systems Letters. 2023, vol. 7, issue 1, p. 2839-2844.en
dc.identifier.doi10.1109/LCSYS.2023.3289608cs
dc.identifier.issn2475-1456cs
dc.identifier.orcid0000-0002-2013-6912cs
dc.identifier.orcid0000-0001-8624-5874cs
dc.identifier.other184309cs
dc.identifier.researcheridA-3448-2009cs
dc.identifier.scopus8448897700cs
dc.identifier.urihttps://hdl.handle.net/11012/244278
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIEEE Control Systems Letterscs
dc.relation.projectIdinfo:eu-repo/grantAgreement/EC/H2020/857306/EU//RICAIP
dc.relation.urihttps://ieeexplore.ieee.org/document/10163935cs
dc.rights(C) IEEEcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2475-1456/cs
dc.subjectBayes methodsen
dc.subjectparameter estimationen
dc.subjectidentificationen
dc.subjectvariational methodsen
dc.titleRecursive Variational Inference for Total Least-Squaresen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-184309en
sync.item.dbtypeVAVen
sync.item.insts2024.01.19 21:38:29en
sync.item.modts2024.01.19 15:12:42en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav automatizace a měřicí technikycs
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Kybernetika a robotikacs
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