Variance reduction for estimating a failure probability with multiple criteria

Andres Alban, Hardik A. Darji, Atsuki Imamura, Marvin K. Nakayama

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

1 Scopus citations


We consider a system subjected to multiple loads with corresponding capacities to withstand the loads, where both loads and capacities are random. The system fails when any load exceeds its capacity, and the goal is to apply Monte Carlo methods to estimate the failure probability. We consider various combinations of variance-reduction techniques, including stratified sampling, conditional Monte Carlo, and Latin hypercube sampling. Numerical results are presented for an artificial safety analysis of a nuclear power plant, which illustrate that the combination of all three methods can greatly increase statistical efficiency.

Original languageEnglish (US)
Title of host publication2016 Winter Simulation Conference
Subtitle of host publicationSimulating Complex Service Systems, WSC 2016
EditorsTheresa M. Roeder, Peter I. Frazier, Robert Szechtman, Enlu Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781509044863
StatePublished - Jul 2 2016
Event2016 Winter Simulation Conference, WSC 2016 - Arlington, United States
Duration: Dec 11 2016Dec 14 2016

Publication series

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


Other2016 Winter Simulation Conference, WSC 2016
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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