Introduction to Bayesian modeling

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 lab course teaches how such models can be implemented in Python — using commonly used modeling packages — and fit to data for Bayesian inference, uncertainty quantification and prediction.

The focus of the course will be on basic Bayesian models, but will also touch upon Bayesian neural networks.

There exists a seminar of the same name which synergizes well, particularly if there has not been previous exposure to probability.

Learning Outcomes

After successful completion, students will

  • Be able to implement basic Bayesian models in Python

  • Will be able to visualise, diagnose and interpret these models

Learning Methods

Hands-on lab course during the semester

Exam

Practical exam

Requirements for awarding of Credit Points

Successful participation in the lab course.

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

Enrollment

From the 1st to the 31st of August 2026 interested students can apply for a place in the lab course. 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 the 16th of September 2026 at the latest. Complete your binding registration by registering for the lab course via FlexNow. Please note that following the above steps is mandatory. Enrollment via FlexNow without prior registration via the central Moodle course is not permitted.

Lecturers

Details

Course type
Lab courses
Credits
3
Term
Winter Term 2026/2027
E-Learning
moodle course available

Dates

Lab course
Takes place every week on Tuesday from 14:00 to 17:00.
First appointment is on 13.08.2026
Last appointment is on 02.02.2027

Requirements

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

There exists a seminar of the same name which synergizes well, particularly if there has not been previous exposure to probability.

We expect fluency in one other programming language and familiarity with concepts like

  • control structures

  • data types

  • functions

  • object-oriented programming

These concepts will not be taught separately.

Furthermore, the course will be taking place in a room without PCs, meaning that students are required to use their own laptops during the course.

Literature:
„Statistical Rethinking“ (2nd edition) by Richard McElreath

About the INI

The Institut für Neuroinformatik (INI) is a research unit of the Faculties of Computer Science and Medicine at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de