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We present general principles for the design and analysis of unbiased MonteCarlo estimators in a wide range of settings. Our estimators posses finitework-normalized variance under mild regularity conditions. We apply ourestimators to various settings of interest, including unbiased optimization inSample Average Approximations, unbiased steady-state simulation of regenerativeprocesses, quantile estimation and nested simulation problems.

Unbiased Multilevel Monte Carlo: Stochastic Optimization, Steady-state Simulation, Quantiles, and Other Applications