Introduction to Bayesian Statistical Learning 2

This course will take place as an online event. The link to the streaming platform will be provided to the accepted registrants only. The course will be held in English.

This course is the continuation of the course “Introduction to Bayesian Statistical Learning”. Although, participation in the latter is not strictly necessary to understand the material of this one, preliminary knowledge in Bayesian modelling, as well as in machine learning and artificial intelligence is a pre-requisite.

The course consists of three parts. The first topic, normalizing flows, explores a class of generative models that facilitate likelihood-free inference. The second topic, diffusion models, introduces students to a powerful class of generative models that excel in modeling sequential data, as well as how they are related to Bayesian framework. The third topic, Gaussian processes, is a versatile tool for Bayesian inference and non-parametric modeling. Gaussian processes provide a flexible framework for modeling complex relationships between variables without assuming a specific functional form.

The main topics are:

• Normalizing flows
• Diffusion models
• Gaussian Processes
• Running models on a Supercomputer

Prerequisites:

Participants should be familiar with principles of Bayesian modeling and AI models (e.g., participation in the course Introduction to Bayesian Statistical Learning I, or similar knowledge).

A personal institutional email address (university/research institution, government agency, organisation, or company) is required to register for JSC training courses. If you don't have an institutional email address, please get in touch with the contact person for this course.

Language:

This course is given in English.

Dates:

5-7 May 2026, 9:00-13:00 each day

Instructors:

Alina Bazarova, Jose Robledo (JSC)
Steve Schmerler (HZDR)