Content and computing outline of two undergraduate Bayesian courses: Tools, examples, and recommendations

Content and computing outline of two undergraduate Bayesian courses: Tools, examples, and recommendations
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两门本科贝叶斯课程的内容和计算大纲:工具、示例和建议

DOI:
10.1002/sta4.452
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发表时间:
2022
期刊:
影响因子:
1.7
通讯作者:
Dogucu, Mine
Dogucu, Mine
中科院分区:
数学4区
文献类型:
--
作者:
Hu, Jingchen;Dogucu, Mine

文献摘要

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本科生贝叶斯教育是最近开始受到关注的一个领域。随着许多教育创新和文章的发表以及越来越多的教学和学习材料的共享,统计教育工作者可能有兴趣将贝叶斯统计纳入本科统计和数据科学课程。在本文中,我们简要概述了我们一直在教授的两门本科贝叶斯课程,并通过比较分析来展示我们方法的异同。我们通过一个工作示例更深入地研究了贝叶斯模型的马尔可夫链蒙特卡罗估计方法的各种选择,并讨论了它们对于有抱负的贝叶斯教育者可能想到的不同计算学习目标的利弊。此外,我们分享课程开发和课程设计的挑战和机遇。本文适合有抱负的贝叶斯教育工作者,他们有兴趣学习向本科生介绍贝叶斯统计的方法。
Undergraduate Bayesian education is an area that has started getting attention lately. As many educational innovations and articles are published and increasingly more teaching and learning materials are shared, statistics educators might be interested in incorporating Bayesian statistics in their undergraduate statistics and data science curriculum. In this paper, we share a succinct overview of two undergraduate Bayesian courses we have been teaching, with a comparison analysis to present the similarities and differences in our approaches. We dive deeper into various choices of Markov chain Monte Carlo estimation methods of Bayesian models with a working example and discuss their pros and cons for different learning objectives of computing that aspiring Bayesian educators might have in mind. Furthermore, we share challenges and opportunities for course development and curriculum design. The paper is suitable for aspiring Bayesian educators who are interested in learning ways to introduce Bayesian statistics to undergraduate students.
DOI: 10.1080/00031305.2022.2089232
发表时间: 2021-09
期刊: The American Statistician
影响因子: --
作者:
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