Abstract
A strategy for efficient evaluation of a complex stochastic model's performance is to use a simpler model's known performance as an approximation. Another strategy is to simulate using the simpler model's known performance as an external control variate. We combine these two strategies. We also relax the assumption that the simpler model's performance is known exactly by introducing mean-squared-error optimal control variates.
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