Resampling methods for input modeling

Russell Richard Barton, Lee W. Schruben

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

Abstract

Stochastic simulation models are used to predict the behavior of real systems whose components have random variation. The simulation model generates artificial random quantities based on the nature of the random variation in the real system. Very often, the probability distributions occurring in the real system are unknown, and must be estimated using finite samples. This paper shows three methods for incorporating the error due to input distributions that are based on finite samples, when calculating confidence intervals for output parameters.

Original languageEnglish (US)
Article number48
Pages (from-to)372-378
Number of pages7
JournalWinter Simulation Conference Proceedings
Volume1
DOIs
StatePublished - Jan 1 2001

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Safety, Risk, Reliability and Quality
  • Chemical Health and Safety
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Resampling methods for input modeling'. Together they form a unique fingerprint.

Cite this