{"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":0.9000,"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}
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.
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.
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