Estimating a failure probability using a combination of variance-reduction techniques

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

3 Scopus citations

Abstract

Consider a system that is subjected to a random load and having a corresponding random capacity to withstand the load. The system fails when the load exceeds capacity, and we consider efficient simulation methods for estimating the failure probability. Our approaches employ various combinations of stratified sampling, Latin hypercube sampling, and conditional Monte Carlo. We construct asymptotically valid upper confidence bounds for the failure probability for each method considered. We present numerical results to evaluate the proposed techniques on a safety-analysis problem for nuclear power plants, and the simulation experiments show that some of our combined methods can greatly reduce variance.

Original languageEnglish (US)
Title of host publication2015 Winter Simulation Conference, WSC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages621-632
Number of pages12
ISBN (Electronic)9781467397438
DOIs
StatePublished - Feb 16 2016
EventWinter Simulation Conference, WSC 2015 - Huntington Beach, United States
Duration: Dec 6 2015Dec 9 2015

Publication series

NameProceedings - Winter Simulation Conference
Volume2016-February
ISSN (Print)0891-7736

Other

OtherWinter Simulation Conference, WSC 2015
Country/TerritoryUnited States
CityHuntington Beach
Period12/6/1512/9/15

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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