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 Data might be outdated or incorrect.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

402-0738-10L 6 Credits DR , MSC D-PHYS
You're viewing possible stale or outdated data. Please check the latest semester for more up-to-date information.

Bayesian Statistical Methods and Data Analysis

Does not take place this semester.
VVZ CR n/a

Last Updated: 2026-06-01 11:31:00

Abstract

The course covers various data analysis methods using Bayesian statistics, with a focus on practical problem solving. We will go over a brief introduction to probability theory, Bayesian reasoning, and how to build a statistical model and compare it to data. The course builds towards analysing data from real astrophysical problems, using both classical statistical methods and machine learning.

Objective

The goal of this course is to introduce students to Bayesian statistics and prepare them to solve statistical inference problems in contemporary (astrophysics) research. After introducing Bayesian statistics and general methodology, the course focusses on building up a structured approach to analyse increasingly complex data and models. The methods are general and applicable beyond (astro)physics, however.

Content

Topics covered include: Review of probability theory: - Independence, joint and conditional probabilities - Univariate and multivariate probability distributions - Change of variables Bayesian statistics: - Bayes’ theorem - Priors - Bayesian reasoning - Posterior distributions, model checking, and model comparison Tools for statistical inference: - Various sampling methods, such as Markov chain Monte Carlo (Metropolis Hastings, slice sampling, Hamiltonian Monte Carlo) and nested sampling - Simulation-based inference - PCA, bootstrap - Gaussian processes and Gaussian random fields - Machine learning and probabilistic programming The lectures are accompanied with code examples, both to illustrate the covered topics and to demonstrate how the theoretical concepts can be implemented in practical computational inference problems. The students complete a project on a statistical analysis, using the tools covered in the course.

General Information

Language
English
Levels
DR , MSC

Examination

Type
graded semester performance

Registration & Places

Max Places
60

Course Components

Type Title Time & Place Hours
lecture with exercise Bayesian Statistical Methods and Data Analysis
Does not take place this semester.
No time listed 3 h weekly

Offered In