Laajuus: 5

Aikataulu: 03.01.2018 - 04.04.2018

Opetusperiodi (voimassa 01.08.2018-31.07.2020): 

III‐IV 2019-2020 (spring)

Osaamistavoitteet (voimassa 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.

Sisältö (voimassa 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.

Toteutus, työmuodot ja arvosteluperusteet (voimassa 01.08.2018-31.07.2020): 

Final exam, home exercises, and project work.

Työmäärä toteutustavoittain (voimassa 01.08.2018-31.07.2020): 

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

Oppimateriaali (voimassa 01.08.2018-31.07.2020): 

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

Kurssin kotisivu (voimassa 01.08.2018-31.07.2020): 

https://mycourses.aalto.fi/course/search.php?search=ELEC-E8105

Esitiedot (voimassa 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".

Arvosteluasteikko (voimassa 01.08.2018-31.07.2020): 

0-5

Ilmoittautuminen (voimassa 01.08.2018-31.07.2020): 

via Weboodi

Lisätietoja (voimassa 01.08.2018-31.07.2020): 

language class 3: English

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