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Simulation-Based Inference: A Practical Guide

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arxiv 2508.12939 v1 pith:P56ZS6NF submitted 2025-08-18 stat.ML cs.LG

Michael Deistler , Jan Boelts , Peter Steinbach , Guy Moss , Thomas Moreau , Manuel Gloeckler , Pedro L. C. Rodrigues , Julia Linhart
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Janne K. Lappalainen Benjamin Kurt Miller Pedro J. Gonçalves Jan-Matthis Lueckmann Cornelius Schröder Jakob H. Macke
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classification stat.MLcs.LG
keywords inferencemethodspracticalapplyastrophysicsbayesiandataempirical
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 9 Pith papers

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    An LLM proposes simulator programs and neural simulation-based inference ranks them, enabling joint model selection and parameter estimation.

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  9. An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning

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    A structured introduction to ML-based simulation-based inference, contrasting Bayesian and frequentist frameworks and covering parameter inference, unfolding, and validation.

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