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.

中文翻译：

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两门贝叶斯本科课程的内容和计算大纲：工具、示例和建议

本科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材料被共享，统计教育工作者可能会对将贝叶斯统计纳入本科统计和数据科学课程感兴趣。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深入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工作者可能想到的不同计算学习目标的优缺点。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，他们有兴趣学习如何向本科生介绍贝叶斯统计。