We provide microfoundations for stochastic frontier analysis. We revise previous work showing that a simple Bayesian learning model supports gamma distributions. The conclusion depends on problem formulation and assumptions about the sampling process and the prior. After a new formulation of the problem the distribution of one-sided error component does not belong to known family. More doubt is cast using expected utility of profit maximization.