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Bayesian variable selection using the hyper‐ g prior in WinBUGS
Author(s) -
Perrakis Konstantinos,
Ntzoufras Ioannis
Publication year - 2018
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1442
Subject(s) - bayesian probability , computer science , selection (genetic algorithm) , feature selection , variable (mathematics) , model selection , bayesian linear regression , artificial intelligence , machine learning , bayesian inference , mathematics , mathematical analysis
The hyper‐ g prior is a default choice for Bayesian variable selection in normal linear regression models. In this article we provide an overview of the Bayesian variable selection framework and explain in detail the specification for the hyper‐ g prior setup. The practical implementation of the methods under consideration is demonstrated through the use of WinBUGS software explaining the correspondence between code and theoretical setup. An illustration of results is considered through a simulated data example. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical Models > Model Selection Statistical Models > Linear Models