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450102 SE COSMOS Topic Seminar: Bayesian Approaches to Inverse Problems in Astrophysics and Cosmology (2024W)
Continuous assessment of course work
Labels
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 05.09.2024 00:00 to Mo 23.09.2024 23:59
- Registration is open from We 25.09.2024 00:00 to Th 03.10.2024 23:59
- Deregistration possible until Th 31.10.2024 23:59
Details
max. 10 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Monday 07.10. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 14.10. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 21.10. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 28.10. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 04.11. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 11.11. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 18.11. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 25.11. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 02.12. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 09.12. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 16.12. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 13.01. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 20.01. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 27.01. 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
Information
Aims, contents and method of the course
Bayesian hierarchical modelling is a powerful and flexible technique for data modelling problems. In this framework, we build probabilistic models of the data generating process (e.g. including physical effects, selection functions, observational noise etc) and use this model to infer unobserved model parameters and to predict unseen data. This approach provides a theoretically principled way to quantify uncertainty, incorporate prior information, and combine datasets.The aim of this course is to equip students with knowledge and computational tools to apply Bayesian methods to problems in astrophysics and cosmology. By the end of the course, students should be able to (i) explain the concepts behind Bayesian, hierarchical modelling, (ii) devise bespoke probabilistic models for data modelling problems, and (iii) execute a range of methods of statistical inference.The course will start with five lectures which introduce students to the theoretical framework and computational tools (specifically, the probabilistic programming language numpyro). Students will then apply this knowledge by splitting into pairs to work on hands-on projects. A number of projects spanning problems in astrophysics and cosmology will be provided by the lecturers. Throughout the course, lecturers will be available to discuss progress and offer advice.
Assessment and permitted materials
Assessment will be out of 100 points:
- 30 points for a final presentation of the project (with slides),
- 60 points for a written project report (7-8 pages),
- 10 points for active participation in discussions and the lectures.
Detailed criteria for evaluating the presentations and written report will be provided in the course.
- 30 points for a final presentation of the project (with slides),
- 60 points for a written project report (7-8 pages),
- 10 points for active participation in discussions and the lectures.
Detailed criteria for evaluating the presentations and written report will be provided in the course.
Minimum requirements and assessment criteria
Students must submit a final presentation and project report, and they must attend all lectures and final presentations (any difficulties should be communicated in advance). Attendance during the period of project-work is not mandatory.
Examination topics
n/a
Reading list
Resources will be discussed in the course.
Association in the course directory
BEN; VAF; PM-Astr; PM-FnNawi;
Last modified: Mo 07.10.2024 11:07