Credits: 5

Schedule: 08.01.2020 - 08.04.2020

Teaching Period (valid 01.08.2018-31.07.2020): 

III‐IV 2019-2020 (spring)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

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.

Content (valid 01.08.2018-31.07.2020): 

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 (valid 01.08.2018-31.07.2020): 

Final exam, home exercises, and project work.

Workload (valid 01.08.2018-31.07.2020): 

Contact teaching 26 h (lectures and exercise sessions), independent studies and project work
110 h, examination 3 h

Study Material (valid 01.08.2018-31.07.2020): 

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

Course Homepage (valid 01.08.2018-31.07.2020):

Prerequisites (valid 01.08.2018-31.07.2020): 

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‐E8104 Stochastic
models and estimation" is recommended, as well as "BECS‐E2601 Bayesian data analysis".

Grading Scale (valid 01.08.2018-31.07.2020): 


Registration for Courses (valid 01.08.2018-31.07.2020): 

via Weboodi

Further Information (valid 01.08.2018-31.07.2020): 

language class 3: English


Registration and further information