Institute for Advanced Simulation (IAS)

Jülich Supercomputing Centre (JSC)

Introduction to Bayesian Statistical Learning (training course, online)

Anfang

24.03.2025 08:00 Uhr

Ende

28.03.2025 12:00 Uhr

Veranstaltungsort

Online

Kontakt

Dr. Alina Bazarova

ai-courses-jsc@fz-juelich.de

Zum Termin anmelden

(Kurs-Nr. 6820251 im Trainingsprogramm 2025 des Forschungszentrums Jülich)

Dieser Kurs findet als Online-Veranstaltung statt. Der Link zur Streaming-Plattform wird nur den angemeldeten Teilnehmern zur Verfügung gestellt.

Inhalt:

When observing data, the key question is: What I can learn from the observation? Bayesian inference treats all parameters of the model as random variables. The main task is to update their distribution as new data is observed. Hence, quantifying uncertainty of the parameter estimation is always part of the task. In this course we will introduce the basic theoretical concepts of Bayesian Statistics and Bayesian inference. We discuss the computational techniques and their implementations, different types of models as well as model selection procedures. We will exercise on the existing datasets use the PyMC3 framework for practicals.

The main topics are:

Bayes theorem
Prior and Posterior distributions
Computational challenges and techniques: MCMC, variational approaches
Models: Mixture Models, Bayesian Neural Networks, Variational Autoencoder, Normalizing Flows
PyMC3 framework for Bayesian computation
Running Bayesian models on a Supercomputer

Contents level	in hours	in %
Beginner's contents:	4.5 h	30 %
Intermediate contents:	10.5 h	70 %
Advanced contents:	0 h	0 %
Community-targeted contents:	0 h	0 %

Voraussetzungen:

Participants should be familiar with general statistical concepts, such as distributions, samples. Furthermore, familiarity with fundamental Machine Learning concepts such as regression, classification and training is helpful.