Comparison of software packages for performing Bayesian inference

IF 0.7 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Network World Pub Date : 2020-01-01 DOI:10.14311/NNW.2020.30.019

M. Koprivica

引用次数: 1

Abstract

In this paper, we compare three state-of-the-art Python packages for Bayesian inference: JAGS [14], Stan [5], and PyMC3 [18]. These packages are in focus because they are the most mature, and Python is among the most utilized programming languages for teaching mathematics and statistics in colleges [13]. The experiment is based on real-world data collected for investigating the therapeutic touch nursing technique [17]. It is analyzed through a hierarchical model with prior beta distribution and binomial likelihood function. The tools are compared by execution time and sample quality.

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执行贝叶斯推理的软件包比较

在本文中，我们比较了用于贝叶斯推断的三个最先进的Python包:JAGS[14]、Stan[5]和PyMC3[18]。这些包之所以受到关注，是因为它们是最成熟的，Python是大学数学和统计学教学中使用最多的编程语言之一。本实验是基于真实世界收集的数据来研究治疗性触摸护理技术[17]。通过先验分布和二项似然函数的层次模型进行分析。比较了两种工具的执行时间和样品质量。

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

Neural Network World 工程技术-计算机：人工智能

CiteScore

1.80

自引率

0.00%

发文量

审稿时长

12 months

期刊介绍： Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.