LS00EN30 Estimation Theory (5 cr)
Description
Probability theory, Bayes estimation, Mean square estimation, Maximum a posteriori estimation, Gauss-Markov estimation, Least squares estimation, time-varying estimation, non-linear estimation, examples.
Learning outcomes
After the course, the student - understands multivariate probability densities and the related parameters - understands the principles of different estimation approaches - is able to derive different types of linear estimators, depending on various different assumptions, for example: ML-estimator, MS-estimator, MAP-estimator, LS-estimator - understands the concept of nonlinear estimation The course develops the following generic skills: critical thinking, identification and development of one's own expertise, interaction and communication. - understands the idea of time-varying (dynamic) estimation - knows the Kalman filter approach in dynamic estimation - is able to write computer code for different types of estimation approaches - can apply the estimators in practical estimation problems
Additional information
The course will be held only every second academic year. You can see the course implementations in Study guide, under “Course Units” or “Show past courses”. This course is intended for the following student groups: - Undergraduate students in Technical Physics (MSc) - Undergraduate students in Applied Physics (MSc)