Unbiased Multilevel Monte Carlo: Stochastic Optimization, Steady-state Simulation, Quantiles, and Other Applications
Statistics Theory
2019-04-23 v1 Optimization and Control
Computation
Statistics Theory
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.
Cite
@article{arxiv.1904.09929,
title = {Unbiased Multilevel Monte Carlo: Stochastic Optimization, Steady-state Simulation, Quantiles, and Other Applications},
author = {Jose H. Blanchet and Peter W. Glynn and Yanan Pei},
journal= {arXiv preprint arXiv:1904.09929},
year = {2019}
}
Comments
20 pages, 2 figures