VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.

447-6273-00L 2 Credits MSC , WBZ D-MATH

Applied Bayesian Statistics

Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to . The Registrar's Office will then register you for the course.
VVZ CR n/a

Last Updated: 2026-06-03 00:07:32

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
MSC , WBZ
Frequency
Every two years

Examination

Type
ungraded semester performance

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture with exercise Applied Bayesian Statistics
Block course Mon 09.11.26 14:15 - 18:00 Mon 16.11.26 14:15 - 18:00 Mon 23.11.26 14:15 - 18:00 Mon 30.11.26 14:15 - 18:00 Mon 07.12.26 14:15 - 18:00 Mon 14.12.26 14:15 - 18:00 Final Examination: 18.01.27
No time listed 21 h semesterly

Offered In