miniKanren as a Tool for Symbolic Computation in Python

TL;DR

This paper explores the use of miniKanren, a relational programming framework, for symbolic computation in Python, highlighting its potential for enhancing mathematical and statistical modeling workflows.

Contribution

It demonstrates how miniKanren can be integrated into Python for symbolic mathematics and term rewriting, and discusses its future potential in statistical modeling and optimization.

Findings

miniKanren enables symbolic computation in Python

It facilitates integration with existing Python libraries

Potential for improved domain-specific optimizations in Bayesian modeling

Abstract

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.