In this article, we give a brief overview of the current state and future potential of symbolic computation within the Python statistical modeling and machine learning community. We detail the use of miniKanren as an underlying framework for term rewriting and symbolic mathematics, as well as its ability to orchestrate the use of existing Python libraries. We also discuss the relevance and potential of relational programming for implementing more robust, portable, domain-specific "math-level" optimizations--with a slight focus on Bayesian modeling. Finally, we describe the work going forward and raise some questions regarding potential cross-overs between statistical modeling and programming language theory.

中文翻译：

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miniKanren 作为 Python 符号计算的工具

在本文中，我们简要概述了 Python 统计建模和机器学习社区中符号计算的当前状态和未来潜力。我们详细介绍了 miniKanren 作为术语重写和符号数学的基础框架的使用，以及它编排使用现有 Python 库的能力。我们还讨论了关系编程在实现更健壮、可移植、特定于领域的“数学级”优化方面的相关性和潜力——稍微关注贝叶斯建模。最后，我们描述了未来的工作，并提出了一些关于统计建模和编程语言理论之间潜在交叉的问题。