Introduction to Bayesian Statistical Learning
5 mei 2026 - 7 mei 2026
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.
Praktische info:
5 mei 2026 - 7 mei 2026
12 uur
Online
Engels
Doelgroep: PhD students and Postdocs
Inschrijven?
- Voorwaarden: Participants should be familiar with principles of Bayesian modeling and AI models; A personal institutional email address is required
- Prijs: Prijs op aanvraag
georganiseerd door:
Leertraject
Main topics:
- Normalizing flows
- Diffusion models
- Gaussian Processes
- Running models on a Supercomputer
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