One of the fundamental problems in Bayesian statistics is the approximation of the posterior distribution. Gibbs sampler and coordinate ascent variational inference are renownedly utilized approximation techniques that rely on stochastic and deterministic approximations. This article aims to clarify the set-theoretical point of view on the two schemes and provide some insights for them.

翻译：Bayesian统计数据的根本问题之一是后方分布的近似值。 Gibbs 采样人和协调上位变异推论者是著名的使用近似技术的近似技术,这些技术依赖随机和确定近似值。 文章旨在澄清关于这两种办法的定点理论观点,并为它们提供一些洞察力。