Probabilistic Programming with Exact Conditions

IF 2.3 2区计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of the ACM Pub Date : 2023-11-11 DOI:10.1145/3632170

Dario Stein, Sam Staton

引用次数: 0

Abstract

We spell out the paradigm of exact conditioning as an intuitive and powerful way of conditioning on observations in probabilistic programs. This is contrasted with likelihood-based scoring known from languages such as Stan . We study exact conditioning in the cases of discrete and Gaussian probability, presenting prototypical languages for each case and giving semantics to them. We make use of categorical probability (namely Markov and CD categories) to give a general account of exact conditioning which avoids limits and measure theory, instead focusing on restructuring dataflow and program equations. The correspondence between such categories and a class of programming languages is made precise by defining the internal language of a CD category.

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具有精确条件的概率规划

我们详细阐述了精确条件反射的范例，作为一种直观而有力的方式，在概率程序中对观察结果进行条件反射。这与斯坦等语言的基于可能性的评分形成了对比。我们研究了离散概率和高斯概率下的精确条件作用，给出了每种情况下的原型语言并给出了语义。我们利用范畴概率(即马尔可夫和CD类别)给出了精确条件的一般说明，避免了限制和度量理论，而不是专注于重构数据流和程序方程。通过定义CD类别的内部语言，可以精确地确定这些类别与一类编程语言之间的对应关系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the ACM 工程技术-计算机：理论方法

CiteScore

7.50

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： The best indicator of the scope of the journal is provided by the areas covered by its Editorial Board. These areas change from time to time, as the field evolves. The following areas are currently covered by a member of the Editorial Board: Algorithms and Combinatorial Optimization; Algorithms and Data Structures; Algorithms, Combinatorial Optimization, and Games; Artificial Intelligence; Complexity Theory; Computational Biology; Computational Geometry; Computer Graphics and Computer Vision; Computer-Aided Verification; Cryptography and Security; Cyber-Physical, Embedded, and Real-Time Systems; Database Systems and Theory; Distributed Computing; Economics and Computation; Information Theory; Logic and Computation; Logic, Algorithms, and Complexity; Machine Learning and Computational Learning Theory; Networking; Parallel Computing and Architecture; Programming Languages; Quantum Computing; Randomized Algorithms and Probabilistic Analysis of Algorithms; Scientific Computing and High Performance Computing; Software Engineering; Web Algorithms and Data Mining