Bayesian model selection for linear regression

Miguel de Benito Delgado, Philipp Wacker

Publication date: 15 December 2015

Abstract: In this note we introduce linear regression with basis functions in order to apply Bayesian model selection. The goal is to incorporate Occam's razor as provided by Bayes analysis in order to automatically pick the model optimally able to explain the data without overfitting.

Has companion code repository: https://github.com/mdbenito/ModelSelection

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian inference (62F15)

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