Do you spend time tuning parameters for complex scientific simulators? Perhaps you use grid search or optimization to match parameters to data. These find a best-fit set, but often don't reveal your confidence or if other parameters also fit. This uncertainty is crucial for reliable conclusions.
This tutorial introduces Simulation-Based Inference (SBI), a modern technique tackling this challenge. Unlike traditional Bayesian inference methods (like MCMC) that require mathematical likelihood functions, SBI works directly with your simulator's outputs. Using recent advances in probabilistic ML, it estimates the probability distribution of parameter values consistent with your observations, even for complex "black-box" simulators. It provides not just a single best guess, but full parameter distributions representing parameter uncertainties and potential interactions.
In this hands-on tutorial using the sbi Python package, you'll learn the practical steps: setting up the problem, running SBI for parameter distributions, and checking result reliability. We will cover different SBI techniques and how to apply them.
If you are a scientist or engineer using Python for simulations, or just interested in probabilistic inference methods, this session is designed for you. Crucially, no prior Bayesian statistics knowledge is required. You will learn to obtain more reliable and interpretable results by quantifying uncertainty and understanding how parameters interact within your model.

This talk introduces a novel approach that bridges Simulation-Based Inference (SBI) and probabilistic programming languages like Pyro to enable simulation-based hierarchical Bayesian inference. SBI is used to perform parameter inference for intractable simulation models, while Pyro facilitates efficient Bayesian inference with complex hierarchical structures. We demonstrate how to integrate SBI-learned likelihoods into Pyro models, allowing for hierarchical Bayesian analysis of simulation-based models. Using the drift-diffusion model from decision-making research as an example, we showcase the potential of this combined approach for tackling real-world problems with complex simulation models and hierarchical data.