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401-3628-14L 4 Credits BSC , DR , MSC , WBZ D-INFK , D-MATH , D-PHYS , D-ITET

Bayesian Statistics

Lecturers & Examiners: Dr. Fabio Sigrist
VVZ CR 2.4

Last Updated: 2026-06-03 00:07:36

Abstract

Introduction to the Bayesian approach to statistics: decision theory, prior distributions, hierarchical Bayes models, empirical Bayes, Bayesian tests and model selection, empirical Bayes, Laplace approximation, Monte Carlo and Markov chain Monte Carlo methods.

Objective

Students understand the conceptual ideas behind Bayesian statistics and are familiar with common techniques used in Bayesian data analysis.

Content

Topics that we will discuss are: Difference between the frequentist and Bayesian approach (decision theory, principles), priors (conjugate priors, noninformative priors, Jeffreys prior), tests and model selection including Bayes factors, hierarchical models and empirical Bayes methods, Bayesian linear regression model, Gaussian processes, computational methods (Laplace approximation, Monte Carlo and Markov chain Monte Carlo methods)

Resources

Lecture Notes

A script will be available in English

Literature

Christian Robert, The Bayesian Choice, 2nd edition, Springer 2007. A. Gelman et al., Bayesian Data Analysis, 3rd edition, Chapman & Hall (2013). Additional references will be given in the course.

General Information

Language
English
Levels
BSC , DR , MSC , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
Closed book; any pocket calculator without communication capability

Course Components

Type Title Time & Place Hours
lecture Bayesian Statistics No time listed 2 h weekly

Offered In