  We explore the performance of sample average approximation in comparison with
several other methods for stochastic optimization when there is information
available on the underlying true probability distribution. The methods we
evaluate are (a) bagging; (b) kernel smoothing; (c) maximum likelihood
estimation (MLE); and (d) a Bayesian approach. We use two test sets, the first
has a quadratic objective function allowing for very different types of
interaction between the random component and the univariate decision variable.
Here the sample average approximation is remarkably effective and only
consistently outperformed by a Bayesian approach. The second test set is a
portfolio optimization problem in which we use different covariance structures
for a set of 5 stocks. Here bagging, MLE and a Bayesian approach all do well.
