Variational Family

Variational families are probability distributions used to approximate complex target distributions in Bayesian inference, aiming to balance accuracy and computational tractability. Current research focuses on developing more expressive families, such as those incorporating hierarchical structures, kernel methods, or mixture models, and on improving optimization algorithms like score matching and proximal methods to efficiently learn their parameters. These advancements are crucial for scaling Bayesian inference to high-dimensional problems in diverse fields, including particle physics, machine learning, and Gaussian process modeling, enabling more accurate and efficient probabilistic modeling.