Bayesian Filtering is a statistical method used to estimate the state of a system over time by updating predictions based on new evidence. It applies Bayes' Theorem, which combines prior knowledge with observed data to improve accuracy. This technique is commonly used in various fields, including robotics, computer vision, and natural language processing. In practice, Bayesian filtering helps in tasks like tracking moving objects or filtering out noise from signals. The most well-known implementation is the Kalman Filter, which is used for linear systems, while the Particle Filter is used for more complex, non-linear scenarios.