Bayesian Computing in the Statistics and Data Science Curriculum

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 transformed Bayesian education at the undergraduate and graduate levels. In this article, we introduce the history behind Bayesian computing, discuss the important role of simulation, and lay out the foundation of Markov chain Monte Carlo. We further survey and weigh various options for implementing Bayesian computational methods in practice. We conclude with computing recommendations for different models of the modern Bayesian classroom, from introductory applied courses to advanced courses with more emphasis on theory.