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

Additional information

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.

Person responsible

Robert Piche

Lessons

Implementation Period Person responsible Requirements
ASE-7536 2016-01 3 - 4 Philipp Muller
Robert Piche

Learning Outcomes

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. implement 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

Content

Content Core content Complementary knowledge Specialist knowledge
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   

Study material

Type Name Author ISBN URL Additional information Examination material
Book   Bayesian Filtering and Smoothing   Simo Särkkä   9781107619289     The PDF is freely available   Yes   

Prerequisites

Course Mandatory/Advisable Description
MAT-02500 Todennäköisyyslaskenta Mandatory    



Correspondence of content

Course Corresponds course  Description 
ASE-7536 Model-Based Estimation, 5-7 cr ASE-5037 Model-Based Estimation, 5-7 cr  

Updated by: Piche Robert, 13.04.2016