用机器学习做模拟推断，解决科学工程中的参数估计难题。
An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning
- 结合贝叶斯与频率学派框架，用神经网络估计后验或似然。
- 同一方法可处理参数估计、去卷积及经验贝叶斯任务。
- 适合需要高效反演复杂模型的科研人员参考。
基于机器学习的模拟推断（SBI）在科学与工程领域解决逆问题中日益重要，涵盖参数推断和探测器效应反演。本文综述了贝叶斯与频率学派统计框架，说明如何利用神经后验估计（NPE）和神经似然估计（NLE）等机器学习方法在这两类框架中进行参数估计，并展示这些方法同样适用于经验贝叶斯或去卷积任务。同时讨论了推断结果的验证方法及当前SBI与机器学习结合的局限性。
原文摘要 · Abstract (English)
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.
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