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.
We present sbi reloaded, a comprehensive update to the sbi Python package that provides researchers with state-of-the-art algorithms and tools for simulation-based inference workflows across scientific domains.
We introduce sbi, a Python package providing a unified interface for simulation-based inference methods, making these powerful techniques accessible to researchers across disciplines.
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.
This dataset contains all irregular verbs in known Romance languages.