The purpose of this paper is to provide an introduction to stochastic frontier models as seen from the point of view of Bayesian analysis. Stochastic frontier models are central in efficiency measurement, and recent advances in Bayesian computation allow us to explore significant extensions of the basic model in a coherent way. In this paper, we describe the fundamentals of efficiency measurement using stochastic frontier models, and describe in reasonable detail the computational aspects of posterior inference and posterior efficiency measurement using the basic model and its extensions.