Proper Posteriors from Improper Priors for an Unidentified Errors-in-Variables Model
研究简单误差变量模型中的推断问题，发现即使模型不可识别，只要先验密度有界（包括不当先验），也能得到恰当后验；以不当均匀先验为例，后验众数恰好是普通最小二乘估计。
The problem considered is inference in a simple errors-in-variables model where consistent estimation is impossible without introducing additional exact prior information.The probabilistic prior information required for Bayesian analysis is found to be surprisingly light: despite the model's lack of identification a proper posterior is guaranteed for any bounded prior density, including those representing improper priors.This result is illustrated with the improper uniform prior, which implies marginal posterior densities obtainable by integrating the likelihood function; surprisingly, the posterior mode for the regression slope is the usual least squares estimate.