Subject description - XP35ESF1
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Explanatory Notes
Instructions
| XP35ESF1 | Estimation and filtering | ||
|---|---|---|---|
| Roles: | PV, S | Extent of teaching: | 2P+2C |
| Department: | 13135 | Language of teaching: | CS |
| Guarantors: | Havlena V. | Completion: | ZK |
| Lecturers: | Havlena V. | Credits: | 4 |
| Tutors: | Havlena V. | Semester: | |
Anotation:
Methodology: experiment design, structure selection and parameter estimation. Bayesian approach to uncertainty description. Posterior probability density function and point estimates: MS, LMS, ML and MAP. Robust numerical implementation of least squares estimation for Gaussian distribution. Parameter estimation and state filtering - Bayesian approach. Kalman filter for white noise. Properties of Kalman filter. Kalman filter for colored/correlated noise.Course outlines:
Exercises outline:
Literature:
Kailath, T. et al., Linear Estimation, Prentice Hall 1999, ISBN 0-13-022464-2Requirements:
Subject is included into these academic programs:| Program | Branch | Role | Recommended semester |
| DKYR_2020 | Common courses | PV | – |
| DOKP | Common courses | S | – |
| DOKK | Common courses | S | – |
| Page updated 14.2.2025 17:51:43, semester: L/2024-5, Z/2025-6, Z/2024-5, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |