Universität Wien

040721 UK Selected Topics in Statistics (MA) (2025W)

3.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
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).

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 07.10. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 14.10. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 21.10. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 28.10. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 04.11. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 11.11. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 18.11. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 25.11. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 02.12. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 09.12. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 16.12. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 13.01. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 20.01. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 27.01. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

Aims:
Get acquainted with concepts of Bayesian statistics: theoretical foundations, methodology and applications.
Learn how to implement computer-based procedures.

Contents:
1. Decision Theory (admissibility and optimality, Bayes and minimax decisions)
2. Bayesian Estimation (Bayes formula, Bayes estimators, hierarchical and empirical Bayes methods)
3. Markov Chain Monte Carlo Methods (Slice Sampler, Gibbs Sampler, Metropolis-Hastings, monitoring convergence, credible intervals)

Methods:
Lecture with exercise sessions.
Lecture notes, exercise sheets and data will be available online.
Students are supposed to code in statistical software.

Assessment and permitted materials

There is a written exam on theoretical topics 1 and 2 on 18.11.2025, as well as three exercise sheets with a programming part on topic 3 (provided on 02.12.2025, 16.12.2025, 20.01.2026). The exercise sheets should be submitted in a week and will be discussed in the exercise session.

The use of AI tools (e.g. ChatGPT) for the production of texts is only permitted if they are expressly requested by the course leader (e.g. for individual work tasks).

Minimum requirements and assessment criteria

40 points for the written exam on topics 1 and 2
20 each exercise sheet

The grade results according to the scheme: 4 from 50 points, 3 from 63 points, 2 from 75 points, and 1 from 87 points.

Examination topics

All topics covered in the lecture.

Reading list

Shao, J. (2003): Mathematical Statistics.
Robert, C. P. And Casella, G. (2004): Monte Carlo Statistical Methods.
Hoff, P. D. (2010): A First Course in Bayesian Statistical Methods.

Association in the course directory

Last modified: Mo 08.09.2025 16:45