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

Understand

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.