Bayesian Inference of Total Least-Squares With Known Precision
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F22%3APU146344" target="_blank" >RIV/00216305:26620/22:PU146344 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9992409" target="_blank" >https://ieeexplore.ieee.org/document/9992409</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CDC51059.2022.9992409" target="_blank" >10.1109/CDC51059.2022.9992409</a>
Alternative languages
Result language
angličtina
Original language name
Bayesian Inference of Total Least-Squares With Known Precision
Original language description
This paper provides a Bayesian analysis of the total least-squares problem with independent Gaussian noise of known variance. It introduces a derivation of the likelihood density function, conjugate prior probability-density function, and the posterior probability-density function. All in the shape of the Bingham distribution, introducing an unrecognized connection between orthogonal least-squares methods and directional analysis. The resulting Bayesian inference expands on available methods with statistical results. A recursive statistical identification algorithm of errors-in-variables models is laid- out. An application of the introduced inference is presented using a simulation example, emulating part of the identification process of linear permanent magnet synchronous motor drive parameters. The paper represents a crucial step towards enabling Bayesian statistical methods for problems with errors in variables.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/TN01000024" target="_blank" >TN01000024: National Competence Center - Cybernetics and Artificial Intelligence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the IEEE Conference on Decision and Control
ISBN
978-1-66-546761-2
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1-6
Publisher name
IEEE
Place of publication
neuveden
Event location
Cancún
Event date
Dec 6, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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