Universität Wien
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040721 UK Selected Topics in Statistics (2020W)

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. 20 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 08.10. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 15.10. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 22.10. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 29.10. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 05.11. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 12.11. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 19.11. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 26.11. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 03.12. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 10.12. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 17.12. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 14.01. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 21.01. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Thursday 28.01. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.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
2. Bayesian Estimation
3. Bayesian Testing
4. Metropolis Hastings Algorithm und Gibbs Sampler
5. Diagnosing Convergence

Methods:
Lectures with exercise sessions.
Lecture notes and data will be available on-line.
Students are supposed to code in statistical software.

Assessment and permitted materials

There is an oral exam on topics 1 to 3 and a written exam with programming part on your own computer on topics 4 and 5.
Both exams will take place in presence.
Additionally, there will be a homework to the topics 1 to 5.

Minimum requirements and assessment criteria

The final grade will be weighted as follows:
35% oral exam on topics 1 to 3
35% written exam on topics 4 and 5
30% homework on all topics 1 to 5

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 28.09.2020 15:27