Bayesian Computing in the Undergraduate Statistics Curriculum

TL;DR

This paper reviews various Bayesian computational methods suitable for undergraduate education, discussing their advantages and disadvantages to guide instructors in selecting appropriate techniques for teaching Bayesian statistics.

Contribution

It provides a comprehensive overview of Bayesian computational options tailored for undergraduate teaching, including practical guidance based on classroom experience.

Findings

Different Bayesian computational methods have distinct pros and cons for teaching.

Guidance is provided for selecting suitable Bayesian methods in undergraduate curricula.

The article emphasizes the importance of aligning computational choices with learning outcomes.

Abstract

Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more accessible to applied statisticians and, in turn, have potentially transformed Bayesian education at the undergraduate level. This article provides an overview on the various options for implementing Bayesian computational methods motivated to achieve particular learning outcomes. The advantages and disadvantages of each computational method are described based on the authors' experience in using these methods in the classroom. The goal is to present guidance on the choice of computation for the instructors who are introducing Bayesian methods in their undergraduate statistics curriculum.