We consider Bayesian error-in-variable (EIV) linear regression accounting for additional additive Gaussian error in the features and response. We construct 3-variable deterministic scan Gibbs samplers for EIV regression models using classical and Berkson errors with independent normal and inverse-gamma priors. We prove these Gibbs samplers are always geometrically ergodic which ensures a central limit theorem for many time averages from the Markov chains.

翻译：我们认为贝叶斯式误差( EIV) 线性回归可以计算出功能和反应中的额外添加性高斯误差。 我们用古典和伯克森型回归模型的误差以及独立的正常和反伽马的前科,为EIV型回归模型建造3种可变确定性Gibs取样器。 我们证明这些吉布斯型取样器总是几何式的异性,可以确保从马尔科夫链链中测出许多时间的中央限值。