Schedule
- 10.1. Overview of Bayesian modeling of time-varying systems (no exercise session before this lecture)
- 17.1. From linear regression to Kalman filter and beyond
- 24.1. Bayesian optimal filtering equations and the Kalman filter
- 31.1. Extended Kalman filter, and statistic linearization
- 7.2. Unscented Kalman filter, Gaussian Filter, GHKF and CKF
- 14.2. (no lecture, no exercise session)
- 21.2. Particle filtering
- 28.2. Rao-Blackwellized particle filtering (no exercise session before this lecture)
- 7.3. Bayesian optimal smoother, Rauch-Tung-Striebel smoothing
- 14.3. Gaussian and particle smoothers
- 21.3. Bayesian estimation of parameters in state space models
- 28.3. Recap of the course topics and project work information
- 8.3. Individual project work starts
- 4.4. Examination
- 8.4. Project deadline