Bayesian Inference of Total Least-Squares With Known Precision
Loading...
Date
2022-09-06
Authors
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Altmetrics
Abstract
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.
Description
Citation
Proceedings of the IEEE Conference on Decision and Control. 2022, p. 1-6.
https://ieeexplore.ieee.org/document/9992409
https://ieeexplore.ieee.org/document/9992409
Document type
Peer-reviewed
Document version
Accepted version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
(C) IEEE