Variational Approximation

Variational approximation is a powerful technique for approximating intractable probability distributions, primarily used in Bayesian inference and machine learning to address computationally expensive posterior calculations. Current research focuses on improving the accuracy and scalability of variational methods, exploring diverse approaches such as entropic regularization, score matching, and novel sparse approximations (e.g., sparse inverse Cholesky factors) within various model architectures including Gaussian processes, variational autoencoders, and neural networks. These advancements enable efficient inference in high-dimensional settings and improve the reliability and accuracy of uncertainty quantification across a wide range of applications, from drug discovery and inverse problems to spatial modeling and phylogenetic inference.