Course Catalog 2014-2015
Postgraduate

Basic Pori International Postgraduate Open University

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Course Catalog 2014-2015

ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr

Additional information

Suitable for postgraduate studies

Person responsible

Robert Piche

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises


 


 
 2 h/week
 2 h/week
+2 h/week
+2 h/week


 
ASE-5036 2014-01 Tuesday 12 - 14 , SC105B
Wednesday 10 - 12 , RD203

Requirements

Exam, homework and computer exercises, term project.
Completion parts must belong to the same implementation

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 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. 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 for random vectors  Laws of total expectation and total variance 
2. Kalman filter  Stationary Kalman filter, information filter, discretisation, robust filter  Bayesian Cramer-Rao bound 
3. EKF, UKF, particle filter  GHKF, CKF, Rao-Blackwellized filter  Optimal importance distribution 
4. RTS smoother  Extensions of RTS smoother for nonlinear systems   Particle smoother 
5. Parameter estimation using EM and MCMC   Identification using particle-EM, Particle-MCMC   

Study material

Type Name Author ISBN URL Edition, availability, ... Examination material Language
Book   Bayesian Filtering and Smoothing   Simo Särkkä   9781107619289       Yes    English  

Prerequisites

Course Mandatory/Advisable Description
ASE-2510 Johdatus systeemien analysointiin Advisable    
ASE-5016 Advanced Methods of Data-driven Modelling and Analysis Advisable    

Additional information about prerequisites
Prerequisites are courses given in Finnish. Thus for this course it is sufficient to know the background in modeling and probability from any suitable course.

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr ASE-5030 Optimal Estimation and Prediction Based on Models, 7 cr  
ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr ACI-42136 Stochastic Estimation and Control, 5 cr  
ACI-21086 Control System Design with Matlab, 5 cr +
ACI-42066 Robust Control, 5 cr +
ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr
ACI-42086 Optimal and Robust Control System Design with Matlab, 7 cr  
ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
ASE-5036 2014-01 Lectures and exercises for the course.   Lectures
Excercises
Practical works
   
Contact teaching: 40 %
Distance learning: 0 %
Self-directed learning: 60 %  

Last modified03.07.2015