Subject description - XP35ESF1

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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 0-13-022464-2

Requirements:

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 15.6.2024 17:51:22, semester: Z/2024-5, Z,L/2023-4, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)