This chapter provides an overview of the Bayesian approach to data analysis, modeling, and statistical decision making. The topics covered go from basic concepts and definitions (random variables, Bayes' rule, prior distributions) to various models of general use in biology (hierarchical models, in particular) and ways to calibrate and use them (MCMC methods, model checking, inference, and decision). The second half of this Bayesian primer develops an example of model setup, calibration, and inference for a physiologically based analysis of 1,3-butadiene toxicokinetics in humans.