🧠 BayesFlow Workshop Materials

Welcome to the official repository for BayesFlow workshop materials!
This repo contains hands-on resources and code examples designed to help you learn and apply modern Bayesian inference techniques using the BayesFlow library.

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📚 Overview

Modern Bayesian inference combines computational tools for estimating, validating, and drawing conclusions from probabilistic models.

In recent years, a new class of simulation-based inference (SBI) methods has emerged as a powerful approach for scaling Bayesian inference to complex models and large datasets. These materials guide you through the foundations and applications of these methods using the BayesFlow library.

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🏗️ Repository Structure

The repository is organized into one folder per workshop, each containing self-contained materials:

• Each workshop folder contains:
  • 📓 Jupyter Notebooks: Interactive code examples and exercises
  • 📁 Data: Supporting datasets (if any)
  • 📄 README: Brief instructions and learning goals for the specific workshop
  • 🛠️ Helpers: Utility functions and modules to simplify common tasks or reduce boilerplate

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⚙️ Getting Started

1. Get the Workshop Materials

You can either clone the repository (if you're familiar with Git) or download it as a ZIP.

🔧 Option 1: Clone with Git (recommended)

git clone https://github.com/your-username/bayesflow-workshops.git
cd bayesflow-workshops

📦 Option 2: Download ZIP

1. Click the green "Code" button
2. Select "Download ZIP"
3. Extract the ZIP file and navigate into the folder

2. Set up the environment

Make sure you have conda installed. Then create the environment from the provided file:

conda env create --file environment.yaml

Activate the environment with:

conda activate bf

3. Start amortizing!

🤝 Contributing

If you find a bug, have suggestions, or want to contribute improvements or additional workshop content, feel free to open an issue or submit a pull request.