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
作者:
Hu, Jingchen;Dogucu, Mine
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.
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DOI:
10.1080/00031305.2022.2089232
发表时间:
2021-09
期刊:
The American Statistician
影响因子:
--
作者:
M. Dogucu;Jingchen Hu
通讯作者:
M. Dogucu;Jingchen Hu
影响因子:
2.2
作者:
M. Bárcena;M. Garín;Ana Martín;F. Tusell;A. Unzueta
通讯作者:
A. Unzueta
DOI:
--
发表时间:
2020
期刊:
影响因子:
--
作者:
Alicia A. Johnson;Colin W. Rundel;Jingchen Hu;Kevin Ross;Allan Rossman
通讯作者:
Allan Rossman
影响因子:
5.8
作者:
Carpenter, Bob;Gelman, Andrew;Riddell, Allen
通讯作者:
Riddell, Allen
DOI:
--
发表时间:
2020
期刊:
影响因子:
--
作者:
A. Hoegh
通讯作者:
A. Hoegh