Gibbs sampler and coordinate ascent variational inference: A set-theoretical review

One of the fundamental problems in Bayesian statistics is approximation posterior distribution. Gibbs sampler and coordinate ascent variational inference are renownedly utilized techniques that rely on stochastic deterministic approximations. In this paper, we define sets densities frequently used inference. We shall be concerned with clarification two schemes from set-theoretical point view. This new way provides an alternative mechanism for analyzing endowed pedagogical insights.