Simulation-Based Inference: A Practical Guide
We provide a practical guide for applying SBI methods, outlining a structured workflow with guidelines and diagnostic tools for every stage—from setting up simulators to validating results.
Neural Networks
Simulated Data is All You Need: Bayesian Parameter Inference for Scientific Simulators with SBI
This talk covers SBI, a method for Bayesian parameter inference using simulated data, and introduces the sbi library, an open-source tool for practitioners and researchers.
Simulation-Based Inference Training
Hands-on training in Simulation-Based Inference (SBI), designed for applications in neuroscience, astrophysics, and biology. Developed in collaboration with the University of Tübingen and the TransferLab at the appliedAI Institute for Europe.
GATSBI: Generative Adversarial Training for Simulation-Based Inference
We introduce GATSBI, a method that combines generative adversarial networks with simulation-based inference to achieve better sample efficiency and accuracy in posterior estimation.