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.
Bayesian Inference
Beyond Likelihoods: Bayesian Parameter Inference for Black-Box Simulators with sbi
A comprehensive hands-on tutorial at EuroSciPy 2025 teaching scientists and engineers how to use simulation-based inference (SBI) for Bayesian parameter estimation in complex simulators, providing full uncertainty quantification beyond simple best-fit approaches.
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.
Flexible and efficient simulation-based inference for models of decision-making
We propose a new method to perform simulation-based inference for mixed data e.g., with continuous and discrete data types, like they often occur in models of decision-making.
sbi: Simulation-Based Inference
sbi is a Python package for simulation-based inference, providing a user-friendly interface to perform Bayesian parameter inference for simulator-based models with intractable likelihoods.