Omfattning: 5

Tidtabel: 03.01.2018 - 04.04.2018

Undervisningsperiod (är i kraft 01.08.2018-31.07.2020): 

III‐IV 2019-2020 (spring)

Lärandemål (är i kraft 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.

Innehåll (är i kraft 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.

Metoder, arbetssätt och bedömningsgrunder (är i kraft 01.08.2018-31.07.2020): 

Final exam, home exercises, and project work.

Arbetsmängd (är i kraft 01.08.2018-31.07.2020): 

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

Studiematerial (är i kraft 01.08.2018-31.07.2020): 

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

Kursens webbplats (är i kraft 01.08.2018-31.07.2020): 

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

Förkunskaper (är i kraft 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".

Bedömningsskala (är i kraft 01.08.2018-31.07.2020): 

0-5

Anmälning (är i kraft 01.08.2018-31.07.2020): 

via Weboodi

Tilläggsinformation (är i kraft 01.08.2018-31.07.2020): 

language class 3: English

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