Recursive Bayesian

Recursive Bayesian methods leverage sequential data to iteratively update probabilistic models, aiming to improve estimations and predictions over time. Current research focuses on applying these methods within diverse frameworks, including variational inference, kernel mean embeddings, and neural networks, to address challenges in areas like online control adaptation, system identification, and intent recognition. This approach offers significant advantages in handling complex, dynamic systems with noisy or incomplete data, impacting fields ranging from robotics and machine learning to signal processing and human-computer interaction.