Bayesian Filtering

Bayesian filtering is a statistical framework for estimating the state of a dynamic system based on noisy observations, aiming to improve accuracy and efficiency in state estimation. Current research focuses on extending Bayesian filtering to high-dimensional and nonlinear systems, often employing techniques like ensemble methods (e.g., Ensemble Kalman Filter, Ensemble Score Filters), variational autoencoders, and convolutional approaches to handle complex data and model mismatches. These advancements are impacting diverse fields, from weather forecasting and robotics to human-computer interaction and particle physics, by enabling more robust and efficient state estimation in complex, real-world scenarios.