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Title: Effect of initial seed and number of samples on simple-random and Latin-Hypercube Monte Carlo probabilities (confidence interval considerations)

Abstract

In order to devise an algorithm for autonomously terminating Monte Carlo sampling when sufficiently small and reliable confidence intervals (CI) are achieved on calculated probabilities, the behavior of CI estimators must be characterized. This knowledge is also required in comparing the accuracy of other probability estimation techniques to Monte Carlo results. Based on 100 trials in a hypothesis test, estimated 95% CI from classical approximate CI theory are empirically examined to determine if they behave as true 95% CI over spectrums of probabilities (population proportions) ranging from 0.001 to 0.99 in a test problem. Tests are conducted for population sizes of 500 and 10,000 samples where applicable. Significant differences between true and estimated 95% CI are found to occur at probabilities between 0.1 and 0.9, such that estimated 95% CI can be rejected as not being true 95% CI at less than a 40% chance of incorrect rejection. With regard to Latin Hypercube sampling (LHS), though no general theory has been verified for accurately estimating LHS CI, recent numerical experiments on the test problem have found LHS to be conservatively over an order of magnitude more efficient than SRS for similar sized CI on probabilities ranging between 0.25 and 0.75.more » The efficiency advantage of LHS vanishes, however, as the probability extremes of 0 and 1 are approached.« less

Authors:
Publication Date:
Research Org.:
Sandia National Labs., Albuquerque, NM (US); Sandia National Labs., Livermore, CA (US)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
756096
Report Number(s):
SAND2000-1131C
TRN: AH200021%%407
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: 8th ASCE Specialty Conference on Probabilistic Mechanics and Structural Reliability, South Bend, IN (US), 07/24/2000--07/26/2000; Other Information: PBD: 4 May 2000
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; MONTE CARLO METHOD; SAMPLING; RANDOMNESS; STATISTICS; DATA COVARIANCES; PROBABILISTIC ESTIMATION

Citation Formats

ROMERO,VICENTE J. Effect of initial seed and number of samples on simple-random and Latin-Hypercube Monte Carlo probabilities (confidence interval considerations). United States: N. p., 2000. Web.
ROMERO,VICENTE J. Effect of initial seed and number of samples on simple-random and Latin-Hypercube Monte Carlo probabilities (confidence interval considerations). United States.
ROMERO,VICENTE J. Thu . "Effect of initial seed and number of samples on simple-random and Latin-Hypercube Monte Carlo probabilities (confidence interval considerations)". United States. https://www.osti.gov/servlets/purl/756096.
@article{osti_756096,
title = {Effect of initial seed and number of samples on simple-random and Latin-Hypercube Monte Carlo probabilities (confidence interval considerations)},
author = {ROMERO,VICENTE J.},
abstractNote = {In order to devise an algorithm for autonomously terminating Monte Carlo sampling when sufficiently small and reliable confidence intervals (CI) are achieved on calculated probabilities, the behavior of CI estimators must be characterized. This knowledge is also required in comparing the accuracy of other probability estimation techniques to Monte Carlo results. Based on 100 trials in a hypothesis test, estimated 95% CI from classical approximate CI theory are empirically examined to determine if they behave as true 95% CI over spectrums of probabilities (population proportions) ranging from 0.001 to 0.99 in a test problem. Tests are conducted for population sizes of 500 and 10,000 samples where applicable. Significant differences between true and estimated 95% CI are found to occur at probabilities between 0.1 and 0.9, such that estimated 95% CI can be rejected as not being true 95% CI at less than a 40% chance of incorrect rejection. With regard to Latin Hypercube sampling (LHS), though no general theory has been verified for accurately estimating LHS CI, recent numerical experiments on the test problem have found LHS to be conservatively over an order of magnitude more efficient than SRS for similar sized CI on probabilities ranging between 0.25 and 0.75. The efficiency advantage of LHS vanishes, however, as the probability extremes of 0 and 1 are approached.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2000},
month = {5}
}

Conference:
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