In the context of optimization under uncertainty, we consider various combinations of distribution estimation and resampling (bootstrap and bagging) for obtaining samples used to acquire a solution and for computing a confidence interval for an optimality gap. This paper makes three experimental contributions to on-going research in data driven stochastic programming: a) most of the combinations of distribution estimation and resampling result in algorithms that have not been published before, b) within the algorithms, we describe innovations that improve performance, and c) we describe open-source software implementations of the algorithms.
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