Course Catalog 2009-2010 Basic Basic Pori International Postgraduate Open University |Degrees|     |Study blocks|     |Courses|

Person responsible

Heikki Huttunen

Implementations

No implementations

Requirements

Final examination, weekly exercises and an assignment.
Completion parts must belong to the same implementation

Principles and baselines related to teaching and learning

-

Learning outcomes

After passing this course the student will understand what estimation is, and how the methods can be applied in signal processing.

Content

Content	Core content	Complementary knowledge	Specialist knowledge
1.	Basic concepts of estimation. What is an estimator and how to compare their performance. Cramer-Rao lower bound for estimator variance.
2.	Classical tools for estimation of a deterministic parameter: minimum variance unbiased estimator (MVUE), sufficient statistic, best linear unbiased estimator (BLUE), least squares estimator.
3.	Estimation of nondeterministic parameters: the Bayesian approach.
4.	Practical signal processing applications will also be considered in each section.

Evaluation criteria for the course

The final grade comes from the final exam. The grade is incremented by one if at least 50% of weekly exercises are done.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Partial passing:

Completion parts must belong to the same implementation

Study material

Type	Name	Author	ISBN	URL	Edition, availability, ...	Examination material	Language
Book	Fundamentals of Statistical Signal Processing - Estimation Theory, Estimation Theory	Kay S. M.	0-13-042268-1		Prentice Hall, 1993		English
Lecture slides	Statistical Signal Processing	Heikki Huttunen	-		-		English

Prerequisites

Additional information about prerequisites
Basic tools of statistics and general knowledge of random variables are required.

Correspondence of content