Introduction to BayesFlow

Follow-up

• Read more about BayesFlow here
• Find the workshop exercises here
• Download the slides here

Type: WorkshopSpeaker: Stefan RadevDate & time: Mon May 12 2025, 13:00-16:00 (CEST)Place: Scheelesalen, Scheele-lab Floor 3, Tomtebodavägen 6URL: https://github.com/bayesflow-org/bayesflow_workshops

This is an in-person event only. No zoom link will be provided.

Required software

Please install Anaconda and a code editor, ideally Visual Studio Code.

Modern Bayesian inference combines computational techniques for estimating, validating, and drawing conclusions from probabilistic models within principled statistical workflows. Recently, a suite of simulation-based inference (SBI) methods has emerged as a key catalyst for scaling these workflows to complex Bayesian models and large data sets.

Stefan’s workshop Simulation-Based Inference With Neural Networks: From Theory to Practice will allow you to get started with this new way of doing Bayesian inference.

Stefan Radev is an assistant professor at Rensselaer Polytechnic Institute and the lead maintainer of the software package BayesFlow.

Why attend?

Fast and scalable Bayes. Simulation-based inferece is a scalable way to fit complex models to large data which would otherwise be extremely difficult with MCMC sampling.

Understand the theory. This workshop will introduce participants to the core theory and concepts of SBI with neural networks, using the BayesFlow software for amortized Bayesian inference. BayesFlow supports multiple backends, including PyTorch, TensorFlow, and JAX, and offers flexible generative networks for sampling, full customization, high-level interfaces, and tools for hyperparameter tuning, design optimization, and estimation from long time series.

Comprehensive, hands-on practice. Attendees will receive a comprehensive overview of the SBI landscape and follow a hands-on path from model building to inference and model-based prediction. The workshop will cover a variety of data types, practical applications, and diagnostic tools.

Guidance for future projects. Participants will gain insights into the strengths and limitations of specific SBI techniques, along with the potential synergies between different approaches and existing software libraries.