Online Bayesian

Online Bayesian methods focus on efficiently updating probabilistic models as new data arrive sequentially, aiming for accurate and adaptable inference without the computational burden of retraining on the entire dataset. Current research emphasizes developing scalable algorithms, such as those based on Kalman filters, particle filters, and Bayesian neural networks, often incorporating low-rank approximations or subspace methods to handle high-dimensional data. These advancements are improving the performance of applications ranging from contextual bandits and time series forecasting to human-in-the-loop systems and privacy-preserving data analysis. The resulting efficiency and adaptability are crucial for real-time decision-making and handling non-stationary data streams in various fields.