A conditional Lindley paradox in Bayesian linear models
贝叶斯线性模型中的条件 Lindley 悖论
The development of prior distributions for Bayesian regression has traditionally been driven by the goal of achieving sensible model selection and parameter estimation. The formalization of properties that characterize good performance has led to the development and popularization of thick-tailed mixtures of g priors such as the Zellner–Siow and hyper-g priors. In this paper we introduce a conditional information asymptotic regime that is motivated by the common data…