执行贝叶斯推理的软件包比较

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

{"title":"执行贝叶斯推理的软件包比较","authors":"M. Koprivica","doi":"10.14311/NNW.2020.30.019","DOIUrl":null,"url":null,"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.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"30 1","pages":"283-294"},"PeriodicalIF":0.7000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Comparison of software packages for performing Bayesian inference\",\"authors\":\"M. Koprivica\",\"doi\":\"10.14311/NNW.2020.30.019\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":49765,\"journal\":{\"name\":\"Neural Network World\",\"volume\":\"30 1\",\"pages\":\"283-294\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Network World\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.14311/NNW.2020.30.019\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Network World","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.14311/NNW.2020.30.019","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 1

摘要

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。

查看原文本刊更多论文

Comparison of software packages for performing Bayesian inference

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

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.