论文标题
概率计划的计划分析
Program Analysis of Probabilistic Programs
论文作者
论文摘要
概率编程是一个增长的领域，旨在通过将概率建模与概率推断分开，以使统计分析更容易访问。在实践中，这种脱钩很困难。没有一个推理算法可以用作同时可靠，高效，黑框和一般的概率编程后端。概率编程语言通常会选择一种应用于给定问题的算法，从而继承其局限性。尽管已经进行了实质性的工作来正式化概率编程和提高推论效率，但几乎没有工作可以通过正式分析可用的程序结构来利用可用的程序结构，以更好地利用基本的推理算法。 本论文提出了三种新技术（静态和动态），旨在通过程序分析改善概率编程。这些技术分析了概率程序并将其调整以使推理更有效，有时以一种乏味或不可能手动做的方式。
Probabilistic programming is a growing area that strives to make statistical analysis more accessible, by separating probabilistic modelling from probabilistic inference. In practice this decoupling is difficult. No single inference algorithm can be used as a probabilistic programming back-end that is simultaneously reliable, efficient, black-box, and general. Probabilistic programming languages often choose a single algorithm to apply to a given problem, thus inheriting its limitations. While substantial work has been done both to formalise probabilistic programming and to improve efficiency of inference, there has been little work that makes use of the available program structure, by formally analysing it, to better utilise the underlying inference algorithm. This dissertation presents three novel techniques (both static and dynamic), which aim to improve probabilistic programming using program analysis. The techniques analyse a probabilistic program and adapt it to make inference more efficient, sometimes in a way that would have been tedious or impossible to do by hand.
