J. H. Blanchet and P. W. Glynn, “Unbiased Monte Carlo for optimization and functions of expectations via multi-level randomization,” 2015 Winter Simulation Conference (WSC), Huntington Beach, CA, USA, 2015, pp. 3656-3667, doi: 10.1109/WSC.2015.7408524.
Abstract
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 suitable growth and finite-moment assumptions. We apply our estimator to various settings of interest, such as optimal value estimation in the context of Sample Average Approximations, and unbiased steady-state simulation of regenerative processes. Other applications include unbiased estimators for particle filters and conditional expectations.
Authors
Jose H Blanchet, Peter W Glynn
Publication date
2015/12/6
Conference
2015 Winter Simulation Conference (WSC)
Pages
3656-3667
Publisher
IEEE
