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We present general principles for the design and analysis of unbiased Monte Carlo estimators for quantities such as α = g(E (X)), where E (X) denotes the expectation of a (possibly multidimensional) random variable X, and g(·) is a given deterministic function. Our estimators possess finite work-normalized variance under mild regularity conditions such as local twice differentiability of g(·) and...
Simulation-based ordinal optimization has frequently relied on large deviations analysis as a theoretical device for arguing that it is computationally easier to identify the best system out of d alternatives than to estimate the actual performance of a given design. In this paper, we argue that practical implementation of these large deviations-based methods need to estimate the underlying large...
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