Prof. Simo Särkkä (firstname.lastname@example.org) and Dr. Angel García-Fernández (email@example.com).
Dr. Roland Hostettler (firstname.lastname@example.org).
Please add "ELEC-E8105" to subject when sending mail concerning the course.
Learning Outcomes: The
student understands the Bayesian basis of estimation in non-linear and
non-Gaussian systems. The student understands the principles behind
approximate filters and smoothers, and is able to use them in practice.
Student knows how to estimate parameters online and offline in
modeling and estimation of non-linear and non-Gaussian systems. Bayesian
filtering and smoothing theory. Extended Kalman filtering and
smoothing, sigma-point and unscented filtering and smoothing, sequential
Monte Carlo particle filtering and smoothing. Adaptive non-linear
filtering; ML, MAP, MCMC, and EM estimation of system parameters.
Example applications from navigation, remote surveillance, and time
Assessment Methods and Criteria: Final exam, home exercises, and project work. The grade of the course is the maximum of the grades of the examination and project work. You need to pass both the examination and the project work to pass the course. To pass the course, you also need to do at least 3/4 of the home exercises. Furthermore if you do (at least) 7/8 of the exercises, your grade increases by one (1 -> 2, 2 -> 3, 3 -> 4, 4 -> 5).
Course Homepage: https://mycourses.aalto.fi/course/view.php?id=13507
of Bayesian inference, multivariate calculus and matrix algebra. Basic
knowledge or ability to learn to use Matlab or Octave is needed for
completing the exercises. "ELEC-E8104 Stochastic models and estimation"
is recommended, as well as "BECS-E2601 Bayesian data analysis".
Grading Scale: 0-5
The course will be taught in English in spring 2017.
The lecture/exercise schedule below is preliminary and might change during the course. Note that the first lecture is on January 11th and there is no exercise session on that day.
- 11.1. Overview of Bayesian modeling of time-varying systems
- 18.1. From linear regression to Kalman filter and beyond
- 25.1. Bayesian optimal filtering equations and the Kalman filter
- 1.2. Extended Kalman filter, statistically linearized filter and Fourier-Hermite Kalman filter
- 8.2. Unscented Kalman filter, Gaussian Filter, GHKF and CKF
- 22.2. Particle filtering
- 1.3. Bayesian optimal smoother, Gaussian and particle smoothers
- 8.3. Bayesian estimation of parameters in state space models
- 15.3. Recap of the course topics and project work information
- 8.3. Individual project work starts
- 5.4. Examination
- 8.4. Project deadline
Recall that before each lecture (except the first one), starting at 3:15 PM, there is an exercise session, starting at 2:15 PM, that you should attend as well.