Sequential Bayesian Inference

Sequential Bayesian inference focuses on efficiently updating probabilistic models as new data arrive, aiming for accurate and computationally feasible estimations in dynamic environments. Current research emphasizes developing improved algorithms, such as variational Bayes and particle filters (often incorporating neural networks for enhanced flexibility), to handle nonlinearities and high-dimensional data in applications like robotics and image processing. These advancements are crucial for real-time applications requiring continuous learning and adaptation, impacting fields ranging from autonomous systems to medical imaging. The development of closed-form solutions and efficient approximations for complex models remains a key focus.