Introduction to Bayesian Statistics
Bayesian statistics offers a unified approach to data analysis problems. The aim of the lecture is to give an initial introduction to the subject using examples from various fields of science and problems from everyday life. Building on the theoretical foundations, issues such as parameter estimation and hypothesis tests as well as their numerical implementation are dealt with.
The lecture will be available as livestream via learnweb! All recordings will be stored in learnweb for later use!
Contact
Dr. O. Kamps, Dr. S. Gurevich
Tel.: +49 251 83-34924,
E-Mail: okamp(at)uni-muenster.de
Date & Room
Do. 10.15-11.45 in room KP/TP 304
Start: 11.04.2024
Contents and Literature
Contents
- Basics of Bayesian statistics
- Markov Chain Monte Carlo
- Parameter estimation
- Hypothesis testing and model selection
- Causal Inference
- Design of experiments
- Bayes and machine learning
- Applications
- Code examples in python and R
Literature
-
D. S. Sivia Data: Analysis - A Bayesian Tutorial
- v. d. Linden, Dose, Toussaint: Bayesian Probability Theory
- C. A.L. Bailer - Jones: Practical Bayesian Inference
Materials
The materials for the lecture are available in the Learnwerb
Lecture materials
The materials for the lecture are available in the Learnwerb