Kalman Filter: State Space Models

This lecture covers the Kalman Filter, a recursive algorithm for estimating the state of a linear dynamic system from a series of noisy measurements. It explains the prediction, updating, and filtering steps of the Kalman Filter, along with the Kalman gain and the state space problem. The lecture also introduces the Extended Kalman Filter and the Unscented Kalman Filter as extensions for non-linear systems. Time series models, state space representation, and practical examples are discussed, emphasizing the importance of normality assumptions and the minimization of prediction errors.