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Lecturer(s)
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Koldovský Zbyněk, prof. Ing. Ph.D.
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Course content
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Lectures and exercises: 1. Random sequences and weakly stationary processes 2. Markov chains I 3. Markov chains II 4. Parameter estimation in Markov chains 5. Unbiased estimate, minimum variance unbiased estimate, linear estimates 6. Cramér-Rao lower bound 7. Maximum likelihood estimation 8. Method of moments 9. Bayes approach I 10. Bayes approach II 11. Minimum mean square error estimation 12. Wiener and Kalman filtering 13. Adaptive LMS filter 14. Adaptive RLS filter
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Learning activities and teaching methods
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Lecture, Practicum, E-learning
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Learning outcomes
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Students will obtain knowledge in the fundamentals of probability theory, statistics, and information theory, and their utilization in the modeling of signals and data.
Theoretical knowledge and practical skills in the given area.
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Prerequisites
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Passed subjects MTI/SEM1, NTI/SEM2 a ITE/MTLB.
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Assessment methods and criteria
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Oral exam, Test
Presence on exercises, sufficient score, passing through a final test.
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Recommended literature
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CH. M. Bischop. Pattern Recognition and Machine Learning. Springer-Verlag, New York, 2011. ISBN 978-0-387-31073-2.
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S. M. Key. Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice-Hall, NJ, USA, 1993. ISBN 0-13-345711-7.
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