Simulation-Based Inference: A Practical Guide
Abstract
A central challenge in many areas of science and engineering is to identify model parameters that are consistent with empirical data and prior knowledge. Bayesian inference offers a principled framework for this task, but can be computationally prohibitive when models are defined by stochastic simulators. Simulation-Based Inference (SBI) provides a suite of methods to overcome this limitation and has enabled scientific discoveries in fields such as particle physics, astrophysics and neuroscience. The core idea of SBI is to train neural networks on data generated by a simulator, without requiring access to likelihood evaluations. Once trained, the neural network can rapidly perform inference on empirical observations without requiring additional optimization or simulations. In this tutorial, we provide a practical guide for practitioners aiming to apply SBI methods. We outline a structured SBI workflow and offer practical guidelines and diagnostic tools for every stage of the process--from setting up the simulator and prior, choosing the SBI method and neural network architecture, training the inference model, to validating results and interpreting the inferred parameters. We illustrate these steps through examples from astrophysics, psychophysics, and neuroscience. This tutorial empowers researchers to apply state-of-the-art SBI methods, facilitating efficient parameter inference for scientific discovery.
