Content

The Bayesian perspective on probability is a cornerstone of modern applied statistics and probabilistic machine learning. Probabilistic models formulated in this framework allow to explicitly communicate and challenge assumptions, perform consistent reasoning, and quantify the uncertainty of predictions — they are a useful tool in data-driven research as well as decision-making.

This seminar aims to explore the conceptual foundations of building these models and employ them for statistical inference and is meant for students without significant prior exposure to the topic and the focus will be on intuitive understanding and rigorous mathematical introduction of probability theory.

The lab course with the same name is recommended for hands-on experience as it is designed for synergy with this course, but this is not a requirement.

Teaching style

The course is based on chapters of book „Statistical Rethinking“ by Richard McElreath with required reading and in-session discussion.

Learning Outcomes

After successful completion, students will

• understand the basic building blocks of Bayesian models

• know different methods for sample-based inference

Learning Methods

Required reading, presentation and discussion of the course book

Exam

Seminar presentation

Requirements for the awarding of Credit Points

Seminar presentation needs to be graded with a passing grade (4,0 or better).

Attendance required. Missing for more than one session will without a medical certificate will result in a „Fail“ for this course.

Enrollment

From 01/02/2026 to 01/03/2026, interested students can apply for a place in the seminar. The allocation of places will take place via the following central Moodle course of the faculty: https://moodle.ruhr-uni-bochum.de/course/view.php?id=62179. The final allocation of places will take place by 16/03/2026 at the latest. Complete your binding registration by registering for the seminar via FlexNow. Please note that following the above steps is mandatory. Enrolment via FlexNow without prior registration via the central Moodle course is not permitted.

Dr. Merlin Schüler Lecturer

merlin.schueler@ini.rub.de NB 3/35

Course type

Seminars

Credits

3

Term

Summer Term 2026

Seminar

Takes place every week on Tuesday from 10:15 to 11:45 in room NB 3/57.
First appointment is on 14.04.2026
Last appointment is on 21.07.2026

Mathematical foundations as taught in the first three semesters of the study programs of the Faculty of Computer Science.