Schedule
- 9.1. Overview of Bayesian modeling of time-varying systems (no exercise session before this lecture) --- in TU1
- 16.1. From linear regression to Kalman filter and beyond-- in TU1
- 23.1. Bayesian optimal filtering equations and the Kalman filter-- in T2
- 30.1. Extended Kalman filter and statistical linearization-- in T2
- 6.2. Unscented Kalman filter, Gaussian Filter, GHKF and CKF-- in T2
- 13.2. Particle filtering & information on project work-- in T2
- 20.2. (no lecture, no exercise session)
- 27.2. Rao-Blackwellized particle filtering-- in T2
- 27.2. Individual project work starts (= project topic selection deadline)
- 6.3. Bayesian optimal smoother, Rauch-Tung-Striebel smoothing-- in T2
- 13.3. Gaussian and particle smoothers-- in T2
- 20.3. Bayesian estimation of parameters in state space models-- in T2
- 27.3. Recap of the course topics-- in T2
- 10.4. Examination-- in place TBA
- 12.4. Project work deadline