标题： 概率程序的静态分析 显示英文标题

标题： Static Analysis for Probabilistic Programs

摘要： 概率编程是一种用于统计机器学习的强大抽象方法。将静态分析方法应用于概率程序可以优化学习过程，自动验证模型属性，并改进用户编程接口。这一针对概率编程的静态分析（SAPP）领域尚处于初期且缺乏组织性，由各种具有不同目标和局限性的技术组成。本研究的主要目的是在一个组织结构和背景下综合SAPP领域的重大贡献。我们提供了静态分析和概率编程的技术背景，建议了一种概率编程语言的功能分类法，并分析了SAPP领域主要思想的适用性。我们得出结论，尽管当前概率程序的静态分析技术存在实际限制，但有许多未来方向具有极大潜力来改善统计机器学习的状态。 显示英文摘要

摘要： Probabilistic programming is a powerful abstraction for statistical machine learning. Applying static analysis methods to probabilistic programs could serve to optimize the learning process, automatically verify properties of models, and improve the programming interface for users. This field of static analysis for probabilistic programming (SAPP) is young and unorganized, consisting of a constellation of techniques with various goals and limitations. The primary aim of this work is to synthesize the major contributions of the SAPP field within an organizing structure and context. We provide technical background for static analysis and probabilistic programming, suggest a functional taxonomy for probabilistic programming languages, and analyze the applicability of major ideas in the SAPP field. We conclude that, while current static analysis techniques for probabilistic programs have practical limitations, there are a number of future directions with high potential to improve the state of statistical machine learning.