This paper present research on end-to-end learning for stochastic optimization, focusing on a Bayesian perspective. The authors propose that the standard algorithm used in this field has a Bayesian interpretation, effectively training a map that performs a posterior Bayes action. Building on this understanding, they introduce new algorithms for training decision-making tools for problems involving empirical risk minimization and distributionally robust optimization. The paper investigates the use of neural networks for creating these decision maps and explores how their architecture influences performance, demonstrating findings through examples like a newsvendor problem and an economic dispatch scenario.