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447-6273-00L 2 Credits WBZ D-MATH
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Applied Bayesian Statistics

Lecturers & Examiners: Dr. Sylvain Robert
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: 2026-02-05 16:01:44

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. On the way we will fit linear models both for continuous and categorical outcomes, and explore techniques to deal with hierarchical structures in the data. 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
The performance assessment takes place on 16 January 2023.

Course Components

Type Title Time & Place Hours
lecture with exercise Applied Bayesian Statistics
Block course. For more information see
  • Mon 14:15-16:00 (HG E 1.2)
  • Mon 16:15-18:00 (HG E 27)
  • 16.01 Date 10:15-12:00 (HG D 1.2)
21 h semesterly

Offered In