Variational Bayesian Inference

Variational Bayesian inference is a powerful technique for approximating intractable posterior distributions in complex probabilistic models, primarily aiming to efficiently estimate model parameters and latent variables. Current research focuses on applying this framework to diverse problems, including time series analysis (using state-space models and neural networks), federated learning (with sparse and clustered Bayesian models), and fault diagnosis (leveraging sparse Bayesian learning). These advancements significantly improve the efficiency and accuracy of Bayesian methods across various fields, impacting areas such as machine learning, signal processing, and system control.