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Simulation-Based Inference A Practical Guide

M Deistler, J Boelts, P Steinbach, G Moss… - arXiv preprint arXiv …, 2025 - arxiv.org
Computer Science paper stat.ML Suggest

… Simulation-based inference, also sometimes referred to as likelihood-free inference, has … discovery with simulation-based inference for scientists in different disciplines. …

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@article{2508.12939v1,
Author        = {Michael Deistler and Jan Boelts and Peter Steinbach and Guy Moss and Thomas Moreau and Manuel Gloeckler and Pedro L. C. Rodrigues and Julia Linhart and Janne K. Lappalainen and Benjamin Kurt Miller and Pedro J. Gonçalves and Jan-Matthis Lueckmann and Cornelius Schröder and Jakob H. Macke},
Title         = {Simulation-Based Inference: A Practical Guide},
Eprint        = {2508.12939v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. 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) is a suite of methods developed to overcome this limitation, which 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, inference is amortized: The neural network can rapidly perform Bayesian inference on empirical observations without requiring additional training 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 and training inference networks, to performing inference and validating the results. 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.},
Year          = {2025},
Month         = {Aug},
Url           = {http://arxiv.org/abs/2508.12939v1},
File          = {2508.12939v1.pdf}
}

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