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Bayesian Data Science
Last Updated: 2026-02-05 15:36:34
Abstract
This course introduces to the Bayesian approach to statistical modeling and further covers on how to formulate and evaluate Bayesian models.
Objective
Students will gain the ability to - understand the difference between frequentist statistics and Bayesian approaches - formalize and implement Bayesian models in R/Stan. - evaluate estimated models.
Resources
Literature
Students are asked to prepare Chapters 2 and 3 of the following book prior to the first course data: Richard McElreath (2016). Statstical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press.
General Information
- Language
- English
- Levels
- DR
- Frequency
- Every two years
Examination
- Type
- ungraded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Bayesian Data Science
Does not take place this semester.
|
No time listed | 6 h semesterly |
Offered In
-
Doctoral Department of Management, Technology, and Economics (More Information at: )