To do so, we first form a family of unnormalized densities (or a "path") parameterized by 2 [0, 1] between the two distributions of interest

A Bayesian pseudocoreset is a compact synthetic dataset summarizing essential information of a large-scale dataset and thus can be used as a proxy dataset for scalable Bayesian inference. Typically, a Bayesian pseudocoreset is constructed by minimizing a divergence measure between the posterior conditioning on the pseudocoreset and the posterior conditioning on the full dataset. However, evaluating the divergence can be challenging, particularly for the models like deep neural networks having high-dimensional parameters.

Stein V ariational Gradient Descent (SVGD) is a nonparametric particle-based deterministic sampling algorithm.

V ariational inference (VI) is a method to approximate the computationally intractable posterior distributions that arise in Bayesian statistics.

Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate.