Abstract

We propose a semester-long Bayesian statistics course for undergraduate students with calculus and probability background. We cultivate students’ Bayesian thinking with Bayesian methods applied to real data problems. We leverage modern Bayesian computing techniques not only for implementing Bayesian methods, but also to deepen students’ understanding of the methods. Collaborative case studies further enrich students’ learning and provide experience to solve open-ended applied problems. The course has an emphasis on undergraduate research, where accessible academic journal articles are read, discussed, and critiqued in class. With increased confidence and familiarity, students take the challenge of reading, implementing, and sometimes extending methods in journal articles for their course projects. Supplementary materials for this article are available online.

摘要

我们为有微积分和概率背景的本科生提供一学期的贝叶斯统计学课程。我们采用适用于实际数据问题的贝叶斯方法来培养学生的贝叶斯思维。我们利用现代贝叶斯计算技术不仅可以实现贝叶斯方法，还可以加深学生对方法的理解。协作案例研究可进一步丰富学生的学习并提供解决开放式应用问题的经验。本课程着重于本科生研究，在课堂上阅读，讨论和评论可访问的学术期刊文章。随着信心和熟悉度的提高，学生将面临阅读，实施和有时扩展期刊文章方法以应对其课程项目的挑战。