Methods and Tools for Bayesian Variable Selection and Model Averaging in Normal Linear Regression
本文回顾了贝叶斯方法在回归模型变量选择中的应用，包括先验设定、后验分布总结和计算策略，并比较了多个R软件包的灵活性和效率，为应用用户提供建议。
Summary In this paper, we briefly review the main methodological aspects concerned with the application of the Bayesian approach to model choice and model averaging in the context of variable selection in regression models. This includes prior elicitation, summaries of the posterior distribution and computational strategies. We then examine and compare various publicly available R ‐packages, summarizing and explaining the differences between packages and giving recommendations for applied users. We find that all packages reviewed (can) lead to very similar results, but there are potentially important differences in flexibility and efficiency of the packages.