Machine Learning

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.

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.

Internal seminar at TransferLab discussing recent advances in simulation-based inference methods and their practical applications across scientific domains.

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.

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.

We introduce GATSBI, a method that combines generative adversarial networks with simulation-based inference to achieve better sample efficiency and accuracy in posterior estimation.

We present a benchmark suite for simulation-based inference, systematically evaluating different methods across tasks with varying dimensionality, simulation budgets, and amortization requirements.

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.