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

arXiv 2607.21702 ↗First posted 23 Jul 2026cs.LG astro-ph.CO astro-ph.GAcited by 1

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Abstract (arXiv)

Simulation-based inference (SBI) with machine learning is an increasingly important tool for solving inverse problems in science and engineering, including parameter inference and the inversion of detector effects. We provide an overview of the Bayesian and frequentist statistical frameworks, describe how machine-learning-based SBI methods, such as neural posterior estimation and neural likelihood estimation, can be used for parameter estimation within these frameworks, and show that the same methods can also be applied to Empirical Bayes or unfolding tasks. We also discuss how to validate inference results and the limitations of SBI with machine learning.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 60 of its 103 references that are in the map. First posted on arXiv 23 Jul 2026.

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60 of 103 references.

References · 60

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