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.
SBI
Pyro Meets SBI: Unlocking Hierarchical Bayesian Inference for Complex Simulators
This talk introduces a novel approach that bridges SBI and Pyro to enable simulation-based hierarchical Bayesian inference, combining SBI’s ability to handle intractable simulators with Pyro’s expressive power for complex hierarchical structures.
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.
sbi reloaded: A toolkit for simulation-based inference workflows
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.
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 for efficient identification of generative models in connectomics
We demonstrate how simulation-based inference can efficiently identify synaptic connectivity rules in dense cortical connectomes, enabling analysis of complex brain circuit organization.
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.