Bayesian Learning

Bayesian learning is a statistical framework that incorporates prior knowledge into machine learning models to improve prediction accuracy and uncertainty quantification, addressing limitations of traditional methods. Current research focuses on scaling Bayesian methods to large datasets and complex models, employing techniques like variational inference, Markov Chain Monte Carlo (MCMC), and novel architectures such as Bayesian neural networks and Bayesian graph neural networks to enhance efficiency and interpretability. This approach is proving valuable across diverse fields, from healthcare diagnostics and drug discovery to robotics and public health, by providing more reliable and trustworthy predictions alongside measures of uncertainty.