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Bayesian linear regression model for curve fitting

conference contribution
posted on 05.03.2019, 00:00 authored by Minmei LiMinmei Li
This article describes a Bayesian-based method for solving curve fitting problems. We extend the basic linear regression model by adding an extra linear term and incorporating the Bayesian learning. The additional linear term offsets the localized behavior induced by basis functions, while the Bayesian approach effectively reduces overfitting. Difficult benchmark dataset from NIST and high-energy physics experiments have been tested with satisfactory results. It is intriguing to notice that curve fitting, a type of traditional numerical analysis problem, can be treated as an adaptive computational problem under the Bayesian probabilistic framework. © IFIP International Federation for Information Processing 2018.

History

Editor

Shi Z; Li J; Mercier-Laurent E

Parent Title

Intelligent Information Processing IX: 10th IFIP TC 12 International Conference, IIP 2018 Nanning, China, October 19–22, 2018 Proceedings

Volume

538

Start Page

363

End Page

372

Number of Pages

10

Start Date

19/10/2018

Finish Date

22/10/2018

eISSN

1868-422X

ISSN

1868-4238

ISBN-13

9783030008277

Location

Nanning, China

Publisher

Springer

Place of Publication

Cham, Switzerland

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Name of Conference

10th IFIP TC 12 International Conference IIP 2018