BARMPy: Bayesian additive regression models Python package

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Danielle Van Boxel
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引用次数: 0

Abstract

We make Bayesian additive regression networks (BARN) available as a Python package, barmpy, with documentation at https://dvbuntu.github.io/barmpy/ for general machine learning practitioners. Our object-oriented design is compatible with SciKit-Learn, allowing usage of their tools like cross-validation. To ease learning to use barmpy, we produce a companion tutorial that expands on reference information in the documentation. Any interested user can pip install barmpy from the official PyPi repository. barmpy also serves as a baseline Python library for generic Bayesian additive regression models.

Abstract Image

BARMPy:贝叶斯加性回归模型 Python 软件包
我们将贝叶斯加性回归网络(BARN)作为一个 Python 软件包(barmpy)提供给广大机器学习从业者,其文档请访问 https://dvbuntu.github.io/barmpy/。我们面向对象的设计与 SciKit-Learn 兼容,允许使用交叉验证等工具。为了方便学习使用 barmpy,我们编写了配套教程,对文档中的参考信息进行了扩展。任何感兴趣的用户都可以从官方 PyPi 代码库中 pip 安装 barmpy。barmpy 还是通用贝叶斯加法回归模型的 Python 基线库。
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来源期刊
Computational Statistics
Computational Statistics 数学-统计学与概率论
CiteScore
2.90
自引率
0.00%
发文量
122
审稿时长
>12 weeks
期刊介绍: Computational Statistics (CompStat) is an international journal which promotes the publication of applications and methodological research in the field of Computational Statistics. The focus of papers in CompStat is on the contribution to and influence of computing on statistics and vice versa. The journal provides a forum for computer scientists, mathematicians, and statisticians in a variety of fields of statistics such as biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge based systems, and Bayesian computing. CompStat publishes hardware, software plus package reports.
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