Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: miniKanren as a Tool for Symbolic Computation in Python   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.

9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc