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Applied Bayesian Statistics
Last Updated: 2026-06-01 11:30:45
Abstract
Introduction to Bayesian statistics: basics of inference, computation with MCMC, linear model, logistic regression, Bayesian hierarchical models. Focus on applications and hands-on programming.
Objective
- understand the basics of Bayesian inference - use R packages to run MCMC algorithms - fit and understand Bayesian linear models - introduction to hierarchical Bayesian models
Content
We will learn how to describe business/scientific problems as probabilistic models, apply Bayes rules to draw inference from data, and use the probabilistic programming language STAN to obtain samples from posterior distributions. We will learn how to build, fit and validate Bayesian models of increasing complexity. There will be examples of applications from various fields: insurance, meteorology, marketing, etc.
Resources
Literature
"Bayes Rules! An Introduction to Applied Bayesian Modeling", Alicia A. Johnson, Miles Q. Ott, Mine Dogucu - CRC Press 2022
General Information
- Language
- English
- Levels
- WBZ
- Frequency
- Every two years
Examination
- Type
- ungraded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Applied Bayesian Statistics
Does not take place this semester.
Block course in HS 2024
|
No time listed | 21 h semesterly |