AI 中文总结
VERaiPHY计划为物理学领域机器学习技术建立统计标准，开篇文章确立相关基础与符号，助力解决ML应用中的统计验证等问题。
AI 中文摘要
现代机器学习正在为基础物理学带来精度、灵活性和计算效率的大幅提升，但统计验证、不确定性量化和稳健性评估的处理却不够系统。VERaiPHY计划（物理学中稳健人工智能的验证与评估）是在PHYSTAT项目框架下开展的一系列文章，旨在为机器学习技术的开发、评估和部署建立统计标准。每篇文章都从统计学视角聚焦特定方法学领域，阐明统计问题、测试方法及结果解读。本开篇文章确立了后续内容所需的概率、统计和机器学习基础，以及贯穿全文的符号体系。
英文摘要
Modern machine learning is leading to substantial gains in precision, flexibility, and computational efficiency in fundamental physics. Statistical validation, uncertainty quantification, and robustness assessment are less systematically addressed. The VERaiPHY initiative (Validation & Evaluation for Robust AI in PHYsics) is a series of articles developed within the PHYSTAT programme, aimed at establishing statistical standards for the development, evaluation, and deployment of ML techniques. Each article focuses on a specific methodological domain from a statistics perspective and clarifies statistical questions, tests, and the interpretation of results. This opening article establishes the probabilistic, statistical, and machine learning foundations that the later contributions assume, together with the notation used throughout.
Comments44 pages, 5 tables
