Introduction to Bayesian Data Analysis

Lecturer (assistant)
  • Linus Hof
TermSommersemester 2024
Language of instructionEnglish
Position within curriculaSee TUMonline


Upon completion of the module, students will understand the role of statistical modeling in drawing inferences from data, have a basic knowledge of Bayesian statistics, be able to analyze data using Bayesian techniques and statistical software (e.g., R, Stan), and be able to integrate basic software engineering techniques (e.g., version control) into their data analysis workflow.


In many academic and industry settings, statistics is not the goal but a tool to learn about a system's regularities from data. These regularities reflect dependency relationships and form the basis of human and organizational understanding, planning, and action. While data contains relevant information about these relationships and forms the basis of learning, it is often complex and vast in amount. We then cannot help but pursue a statistical approach. In this course, you'll learn to learn with the help of Bayesian statistics. Specifically, the course will introduce you to the following topics: - Setting up a reproducible data analysis workflow with R, Git & GitHub - Concepts and intuitions behind Bayesian data analysis - Generative data simulations - Linear Models - Generalized Linear Models - Hierarchical (Multilevel) Modeling