AI 中文总结
介绍基于机器学习的模拟推理，概述贝叶斯和频率主义统计框架，阐述相关SBI方法用于参数估计及经验贝叶斯或展开任务，探讨推理结果验证及该方法局限性。
AI 中文摘要
基于机器学习的模拟推理（SBI）是解决科学与工程中反问题的重要工具，涵盖参数推断和探测器效应反演。本文概述了贝叶斯和频率主义统计框架，描述基于机器学习的SBI方法（如神经后验估计和神经似然估计）在这些框架内用于参数估计的方式，表明相同方法也可用于经验贝叶斯或展开任务。还讨论了如何验证推理结果及SBI的局限性。
英文摘要
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.
Comments39 pages, 13 figures
