Course content

Least-squares adjustments, Necessary linear algebra and theory of Probability. Different models for advanced parameter estimation, Filtering and prediction. Hypothesis testing and reliability analysis, Interval estimation and outlier detection and elimination etc. Spectral analysis.

Learning outcome

Learning objective: After completing the course the candidate shall have an in-depth understanding of parameter estimation and reliability theory of geodetic measurements. Knowledge: After completing the course, the candidate shall have knowledge about Hypothesis testing and reliability analysis of geodetic measurements, knowledge of methods and techniques for advanced parameter, Knowledge of spectral analysis of geodetic and non-geodetic related time series. Skills: After completing the course, the candidate shall be able to perform advanced least squares adjustment of different geodetic observation models, be able to do filtering and prediction, be able to do hypothesis testing and outlier detection, Fourier analysis of time series. General competence: After completing the course the candidate shall have obtained ability to work independently and in a team towards relevant research front in the approximation and estimation of Geo-related parameters, ability to analyze critically the estimated/predicted parameters.

Learning methods and activities

The course will be given as colloquium or as conducted self-tuition, with one project that must be approved before the exam.

Compulsory assignments

• Exercise

Specific conditions

Recommended previous knowledge

Calculus 4N (TMA4125/TMA4130) and Multivariate Analysis (TMA4270).

Required previous knowledge

Master in geomatics. TBA4245 Geodesy or equivalent (some knowledge of geodetic parameters and observations). The course will be run if at least 3 students with relevant background wish to attend. If you wish to attend, please contact the course coordinator. Students not admitted to the PhD programme in engineering may be approved by the course coordinator.

Course materials

Karl Rudolf Koch: Parameter Estimation and Hypothesis Testing in Linear Models. Springer-Verlag Berlin Heidelberg 1999. 333 pages. Recent articles.

Credit reductions