Efficient Input Uncertainty Quantification Via Green Simulation Using Sample Path Likelihood Ratios

Ben M. Feng, Eunhye Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Bootstrapping is a popular tool for quantifying input uncertainty, inflated uncertainty in the simulation output caused by finite-sample estimation error in the input models. Typical bootstrap-based procedures have a nested simulation structure that requires B × R simulation runs: the outer loop bootstraps B input distributions, each of which requires R inner simulation runs. In this article, we present a measure-theoretic framework for constructing a sample path likelihood ratio and propose an efficient input uncertainty quantification procedure using two green simulation estimators. The proposed procedures reuse the same R inner simulation outputs in all outer loops by reweighting them using appropriately defined likelihood ratios. Both procedures produce asymptotically valid confidence intervals for the expected simulation output under the true input distribution. Our numerical results show that the proposed procedures have efficiency gains compared to other popular bootstrap-based alternatives.

Original languageEnglish (US)
Title of host publication2019 Winter Simulation Conference, WSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781728132839
StatePublished - Dec 2019
Event2019 Winter Simulation Conference, WSC 2019 - National Harbor, United States
Duration: Dec 8 2019Dec 11 2019

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2019 Winter Simulation Conference, WSC 2019
Country/TerritoryUnited States
CityNational Harbor

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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