Topic outline

  • Lecturers:
    Prof. Simo Särkkä (

    Co-lecturers / assistants:
    M.Sc. Filip Tronarp (

    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 non-linear systems.

    Statistical 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 series analysis.

    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 1/2 of the home exercises. Furthermore if you do (at least) 3/4 of the exercises, your grade increases by one (1 -> 2, 2 -> 3, 3 -> 4, 4 -> 5).

    Study Material:
    Särkkä: Bayesian Filtering and Smoothing (2013), handouts.

    Course Homepage:

    Basics 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-E8740 Basics of sensor fusion" is recommended, and "CS-E5710 Bayesian data analysis" can be useful.

    Grading Scale: 0-5

    The course will be taught in English in spring 2019.