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280511 VU PM-FnNawi Statistical Methods in Astronomy (PI) (2017W)

6.00 ECTS (3.00 SWS), SPL 28 - Erdwissenschaften, Meteorologie - Geophysik und

Continuous assessment of course work

Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Registration is open from Th 14.09.2017 10:00 to Th 28.09.2017 23:59
Registration is open from Tu 03.10.2017 10:00 to Th 19.10.2017 23:59
Deregistration possible until Th 19.10.2017 23:59

Details

max. 25 participants

Language: English

Lecturers

Oliver Czoske

Classes (iCal) - next class is marked with N

Wednesday 11.10. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 18.10. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 25.10. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 08.11. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 15.11. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 22.11. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 29.11. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 06.12. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 13.12. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 10.01. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 17.01. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 24.01. 13:15 - 14:45 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Wednesday 31.01. 13:15 - 14:45 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17

Information

Aims, contents and method of the course

The lecture course gives an introductory overview of statistical methods that are important for astronomical research: - basics of probability theory - frequentist vs. Bayesian statistics - parameter estimation, robust methods - linear and non-linear regression, model fitting and model selection - density estimation - clustering and classification - time series analysis. Practical examples will be presented using R (software environment for statistical computing; prior knowledge is not required).

Assessment and permitted materials

Lecture attendance - exercises - end-of-term exam

Minimum requirements and assessment criteria

Examination topics

Reading list

Association in the course directory

Emphasis (38 ECTS)

Master Astronomy (861 [3] - Version 2016)

Last modified: Mo 07.09.2020 15:42