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arXiv · 1904.09929

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

Jose H. Blanchet·Peter W. Glynn·Yanan Pei
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Abstract

We present general principles for the design and analysis of unbiased Monte Carlo estimators in a wide range of settings. Our estimators posses finite work-normalized variance under mild regularity conditions. We apply our estimators to various settings of interest, including unbiased optimization in Sample Average Approximations, unbiased steady-state simulation of regenerative processes, quantile estimation and nested simulation problems.

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Jose H. Blanchet, Peter W. Glynn, Yanan Pei. 2019-04-22. Unbiased Multilevel Monte Carlo: Stochastic Optimization, Steady-state Simulation, Quantiles, and Other Applications. https://arxiv.org/abs/1904.09929

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