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. In this paper, we define fundamental sets of densities frequently used in Bayesian inference. We shall be concerned with the clarification of the two schemes from the set-theoretical point of view. This new way provides an alternative mechanism for analyzing the two schemes endowed with pedagogical insights.

翻译：Bayesian 统计数据的根本问题之一是后方分布的近似值。 Gibbs 采样人和协调的升温变异性推断法是著名的使用近似法,这些近似法依赖于随机和确定性近似值。在本文件中,我们界定了Bayesian 推理经常使用的基本密度组。我们将关注从设定理论的角度澄清这两个方案。这一新方式为分析具有教学洞察力的两种方案提供了替代机制。