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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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