Variational Bayes

Variational Bayes (VB) is an approximate Bayesian inference method aiming to efficiently estimate probability distributions, particularly in complex models where exact inference is intractable. Current research focuses on applying VB to diverse areas, including high-dimensional regression, time series forecasting, and deep learning architectures like variational autoencoders (VAEs) and Bayesian neural networks, often incorporating techniques like natural gradient descent and mean-field approximations for improved scalability and accuracy. The resulting advancements in uncertainty quantification and robust inference have significant implications for various fields, enabling more reliable predictions and improved model interpretability in applications ranging from image processing and signal processing to causal inference and federated learning.