ASE-7536 Model-Based Estimation, 5-7 cr


Requirement for 7 credits is solution of homework problems, passing the exam, and writing a term project paper. Requirement for 5 credits: either (exercises and exam) or (exercises and term project).
Suitable for postgraduate studies.


Robert Piche


Toteutuskerta Periodi Vastuuhenkilö Suoritusvaatimukset
ASE-7536 2018-01 3 - 4 Robert Piche
Students are required to solve 2/3 of the homework exercises.

In this implementation only the 5 credit version of the course (exercises and exam) is offered. There is no term project.


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): the 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     The PDF is freely available   Yes   


Opintojakso P/S Selite
MAT-02506 Probability Calculus Mandatory    


Opintojakso Vastaa opintojaksoa  Selite 
ASE-5030 Optimal Estimation and Prediction Based on Models, 7 cr +
ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr +
ASE-7536 Model-Based Estimation, 5-7 cr
MAT-61706 Bayesian Filtering and Smoothing, 5 cr  
ASE-7536 Model-Based Estimation, 5-7 cr ASE-5037 Model-Based Estimation, 5-7 cr  

Päivittäjä: Heinola-Lepistö Johanna, 14.03.2018