Skip to Main content Skip to Navigation
Conference papers

Bayesian Linear Regression Model for Curve Fitting

Abstract : 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [11 references]  Display  Hide  Download
Contributor : Hal Ifip <>
Submitted on : Tuesday, July 30, 2019 - 5:00:42 PM
Last modification on : Tuesday, July 30, 2019 - 5:12:30 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Michael Li. Bayesian Linear Regression Model for Curve Fitting. 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. pp.363-372, ⟨10.1007/978-3-030-00828-4_37⟩. ⟨hal-02197772⟩



Record views


Files downloads