Bayesian Computing in the Undergraduate Statistics Curriculum

Autor/inn/en	Albert, Jim; Hu, Jingchen
Titel	Bayesian Computing in the Undergraduate Statistics Curriculum
Quelle	In: Journal of Statistics Education, 28 (2020) 3, S.236-247 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1069-1898
DOI	10.1080/10691898.2020.1847008
Schlagwörter	Bayesian Statistics; Computation; Statistics Education; Undergraduate Students; Computer Software; Learning Activities; Monte Carlo Methods; Equations (Mathematics); Coding; Programming + Suchen Sie Ihr Suchwort? Lernaktivität; Monte-Carlo-Methode; Equations; Mathematics; Gleichungslehre; Codierung; Programmierung
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 of the various options for implementing Bayesian computational methods motivated to achieve particular learning outcomes. For each computational method, we propose activities and exercises, and discuss each method's pedagogical advantages and disadvantages based on our experience 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. Supplementary materials for this article are available online. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01