The use of Bayesian econometrics as a research tool has exploded over recent decades, but, despite this explosion, it is absent from many introductory econometrics’ courses, often being introduced only as an advanced specialist course. The objective of this paper is to provide a basis for including some Bayesian econometrics in introductory econometrics courses at the level of Hill et al. (2018). It is assumed students have had a prior course in statistics that gives them some knowledge of probability and distributions. Matrix algebra is used sparingly; some examples will require further explanation if students do not have a matrix algebra background. Topics covered are (1) characteristics of the Bayesian approach that distinguish it from the frequentist approach, (2) the requirement for simulation when analytical approaches are inadequate, (3) posterior distributions of nonlinear functions of parameters, (4) the Metropolis algorithm, and (5) Gibbs sampling. Simple examples taken from Hill et al. (2018) are used to illustrate the various concepts; EViews code is provided for each of the examples. Such code will be particularly useful for courses that use EViews as their main software platform.