论文标题
Gibbs采样器和协调上升变异推断：一定的理论综述
Gibbs sampler and coordinate ascent variational inference: a set-theoretical review
论文作者
论文摘要
贝叶斯统计的基本问题之一是后验分布的近似值。 Gibbs采样器和坐标上升变异推理是依赖于随机和确定性近似值的近似技术。在本文中，我们定义了贝叶斯推断中经常使用的基本密度集。从集合理论的角度来看，我们将关注对这两个方案的澄清。这种新方法提供了一种替代机制，用于分析具有教学见解的两个方案。
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.
