MAT-61706 Bayesian Filtering and Smoothing, 5 cr


Suitable for postgraduate studies.


Robert Piche


Toteutuskerta Periodi Vastuuhenkilö Suoritusvaatimukset
MAT-61706 2019-01 3 - 4 Mostafa Mansour
Robert Piche
Solving weekly homework problems, participation in the weekly exercise sessions, and successful completion of the take-home final exam.


After completing the course, the student can apply modern algorithms of Bayesian filtering and smoothing. Student is capable of (grade (3/5)) 1. using the basic concepts and formulas of probability and Bayesian statistical inference. 2. presenting a model-based time-series estimation problem in a state-space form and understanding its statistical assumptions and limitations. 3. implementing the Kalman filter and the most common approximations of the nonlinear Bayesian filter and smoother. 4. understanding the approximations and limitations of different non-linear filters. 5. implementing computations and interpret results for estimating static parameters of the state space model. Grade (1/5): goal 4 and at least two other goals achieved


Sisältö Ydinsisältö Täydentävä tietämys Erityistietämys
1. Multivariate probability basics and the multivariate Gaussian distribution.   Chebyshev inequality  Laws of total expectation and total variance 
2. Kalman filter  Stationary Kalman filter, information filter, treatment of missing measurement  discretisations of stochastic differential equation; Joseph formula 
3. EKF, UKF, bootstrap particle filter  EKF2, GHKF, importance sampling, SIR  stratified resampling, RB particle filter 
4. Bayesian fixed-interval filtering, RTS smoother  RTS extensions; particle smoother   fixed-lag smoothing; fixed-point smoothing 
5. State-space model parameter estimation using MCMC   State space model parameter estimation using EM   


Tyyppi Nimi Tekijä ISBN URL Lisätiedot Tenttimateriaali
Book   Bayesian Filtering and Smoothing   Simo Särkkä   9781107619289     PDF is freely available.   Yes   


Opintojakso P/S Selite
MAT-02506 Probability Calculus Mandatory    
MAT-61806 Optimisation and Statistical Data Analysis Advisable    

Tietoa esitietovaatimuksista
Prerequisite knowledge: matrix algebra, probability, Matlab programming


Opintojakso ei vastaan mitään toista opintojaksoa

Päivittäjä: Piche Robert, 24.05.2019