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SGN-2606 Statistical Signal Processing, 5 cr |
Heikki Huttunen
| Lecture times and places | Target group recommended to | |
| Implementation 1 |
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Final examination, weekly exercises and an assignment.
Completion parts must belong to the same implementation
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After passing this course the student will understand what estimation is, and how the methods can be applied in signal processing.
| 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. |
The final grade comes from the final exam. The grade is incremented by one if at least 50% of weekly exercises are done.
Numerical evaluation scale (1-5) will be used on the course
| 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 |
| Course | O/R |
| MAT-33310 Tilastomatematiikka | Recommended |
| SGN-1200 Signaalinkäsittelyn menetelmät | Obligatory |
| Course | Corresponds course | Description |
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| Description | Methods of instruction | Implementation | |
| Implementation 1 | Lectures Excercises Practical works |
Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 % |