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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.12939 |
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| _version_ | 1866908493110312960 |
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| author | Deistler, Michael Boelts, Jan Steinbach, Peter Moss, Guy Moreau, Thomas Gloeckler, Manuel Rodrigues, Pedro L. C. Linhart, Julia Lappalainen, Janne K. Miller, Benjamin Kurt Gonçalves, Pedro J. Lueckmann, Jan-Matthis Schröder, Cornelius Macke, Jakob H. |
| author_facet | Deistler, Michael Boelts, Jan Steinbach, Peter Moss, Guy Moreau, Thomas Gloeckler, Manuel Rodrigues, Pedro L. C. Linhart, Julia Lappalainen, Janne K. Miller, Benjamin Kurt Gonçalves, Pedro J. Lueckmann, Jan-Matthis Schröder, Cornelius Macke, Jakob H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12939 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulation-Based Inference: A Practical Guide Deistler, Michael Boelts, Jan Steinbach, Peter Moss, Guy Moreau, Thomas Gloeckler, Manuel Rodrigues, Pedro L. C. Linhart, Julia Lappalainen, Janne K. Miller, Benjamin Kurt Gonçalves, Pedro J. Lueckmann, Jan-Matthis Schröder, Cornelius Macke, Jakob H. Machine Learning 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. |
| title | Simulation-Based Inference: A Practical Guide |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.12939 |