Total least squares from a Bayesian perspective: Incorporating data-informed forgetting
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F24%3APU156314" target="_blank" >RIV/00216305:26620/24:PU156314 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10885920/metrics#metrics" target="_blank" >https://ieeexplore.ieee.org/document/10885920/metrics#metrics</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CDC56724.2024.10885920" target="_blank" >10.1109/CDC56724.2024.10885920</a>
Alternative languages
Result language
angličtina
Original language name
Total least squares from a Bayesian perspective: Incorporating data-informed forgetting
Original language description
The real-time estimation of error-in-variables (EIV) models with unknown time-varying parameters is considered and resolved using a Bayesian framework. The stochastic model under consideration is a regression-type model that accounts for inherently inaccurate measurements, which are corrupted by the normal noise. The EIV model identification is traditionally performed via total least squares (TLS), relying on computationally intensive methods to numerically obtain a point estimate. Such a concept, despite its theoretical appeal, nevertheless lacks the ability to quantify the uncertainty associated with the parameter estimates. Thus, this limitation hinders the concept from being combined with the statistical decision-making strategies. The paper opens the way towards enriching the standard TLS in this respect. The enrichment is achieved by projecting the unnormalized posterior generated by the EIV parametric models onto the normal-Wishart distribution. This projection is made optimal by minimizing the Kullback-Leibler distance between the unnormalized and the normal-Wishart posteriors while imposing a hard equality constraint on the mean parameter scalar product. By establishing credible intervals for both the regression parameters and the noise precision, the resultant procedure is additionally endowed with Bayesian data-informed forgetting, which allows for effective operation in nonstationary environments.
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
63th IEEE Conference on Decision and Control
ISBN
979-8-3503-1633-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
5737-5744
Publisher name
IEEE
Place of publication
NEW YORK
Event location
Milano
Event date
Dec 16, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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