We propose and implement a Bayesian learning model for performance. The model implies a specific distribution for performance / technical inefficiency which we exploit in the context of stochastic frontier models. As the theoretical model is ambiguous with respect to what constitutes existing “experience”, we propose and implement alternative specifications. The estimation and inference techniques are based on Bayesian analysis using Markov Chain Monte Carlo methods. We apply the new techniques to a data set of large U.S. banks. Our findings indicate that there is some learning in technical inefficiency although there is limited evidence, if at all, that jumps in experience are related to productivity growth. However, this effect is distinctly pronounced for the 2007-2010 period but much less significant afterwards.