Introduction to Bayesian Statistics

Bayesian statistics offers a unifying framework for addressing problems in data analysis and serves as a theoretical foundation for many methods in machine learning. The aim of the course is to provide an introductory overview of the subject through examples from various scientific disciplines, everyday situations, and current applications in machine learning. Building on the theoretical foundations, the course covers topics such as parameter estimation, model comparison, hypothesis testing, causal inference, and experimental design, complemented by their numerical implementation using modern Monte Carlo methods.

Current:

Further informations about the lecture in summer term 2025 can be found on the Learnweb