Publication detail

Bayesian Inference of Total Least-Squares With Known Precision

FRIML, D. VÁCLAVEK, P.

Original Title

Bayesian Inference of Total Least-Squares With Known Precision

Type

conference paper

Language

English

Original 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.

Keywords

Bayesian networks; Gaussian noise (electronic); Inference engines; Least squares approximations; Permanent magnets

Authors

FRIML, D.; VÁCLAVEK, P.

Released

6. 9. 2022

Publisher

IEEE

ISBN

978-1-66-546761-2

Book

Proceedings of the IEEE Conference on Decision and Control

Pages from

1

Pages to

6

Pages count

6

URL

https://ieeexplore.ieee.org/document/9992409

Full text in the Digital Library

http://hdl.handle.net/11012/209144

BibTex

@inproceedings{BUT180119,
  author="Dominik {Friml} and Pavel {Václavek}",
  title="Bayesian Inference of Total Least-Squares With Known Precision",
  booktitle="Proceedings of the IEEE Conference on Decision and Control",
  year="2022",
  pages="6",
  publisher="IEEE",
  doi="10.1109/CDC51059.2022.9992409",
  isbn="978-1-66-546761-2",
  url="https://ieeexplore.ieee.org/document/9992409"
}