A 39-page review explains Bayesian and frequentist simulation-based inference with ML
burny_tech · x · 2026-07-28
A 39-page overview of simulation-based inference with machine learning
The paper introduces simulation-based inference (SBI) as a machine-learning tool for inverse problems in science and engineering, including parameter inference and detector-effect inversion.
It covers:
- Bayesian and frequentist statistical frameworks
- SBI methods such as neural posterior estimation and neural likelihood estimation
- How the same techniques apply to Empirical Bayes and unfolding tasks
- How to validate inference results and the key limitations of SBI with machine learning
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