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  • In this paper, we suggest principles of a novel simulation method for analyses of functions g(X) of a random vector X, suitablefor the cases when the evaluation of g(X) is very expensive. The method is based on Latin Hypercube Sampling strategy. The paper explains how the statistical, sensitivity and reliability analysis of g(X) can be divided into a hierarchical sequence of simulations with (subsets of samples of a random vector X) such that (i) the favourable properties of LHS are retained (low number of simulations needed for significant estimations of statistics of g(X) with a low variability of the estimation); (ii) all subsets can anytime be merged into one set while keeping its consistency (i.e. the simulation process can be halted e.g., when reaching a certain prescribed statistical significance of the estimations). An important aspect of the method is that it efficiently simulates subsets samples of random vectors with focus on their correlation structure. The procedure is quite general an
  • In this paper, we suggest principles of a novel simulation method for analyses of functions g(X) of a random vector X, suitablefor the cases when the evaluation of g(X) is very expensive. The method is based on Latin Hypercube Sampling strategy. The paper explains how the statistical, sensitivity and reliability analysis of g(X) can be divided into a hierarchical sequence of simulations with (subsets of samples of a random vector X) such that (i) the favourable properties of LHS are retained (low number of simulations needed for significant estimations of statistics of g(X) with a low variability of the estimation); (ii) all subsets can anytime be merged into one set while keeping its consistency (i.e. the simulation process can be halted e.g., when reaching a certain prescribed statistical significance of the estimations). An important aspect of the method is that it efficiently simulates subsets samples of random vectors with focus on their correlation structure. The procedure is quite general an (en)
Title
  • Extension of sample size in Latin Hypercube Sampling with correlated variables
  • Extension of sample size in Latin Hypercube Sampling with correlated variables (en)
skos:prefLabel
  • Extension of sample size in Latin Hypercube Sampling with correlated variables
  • Extension of sample size in Latin Hypercube Sampling with correlated variables (en)
skos:notation
  • RIV/00216305:26110/10:PU91705!RIV11-GA0-26110___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA103/08/0752), P(KJB201720902)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 258548
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26110/10:PU91705
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Latin Hypercube Sampling (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [EA17EF1AA3EB]
http://linked.open...v/mistoKonaniAkce
  • Singapur
http://linked.open...i/riv/mistoVydani
  • Neuveden
http://linked.open...i/riv/nazevZdroje
  • proc. of 4th International Workshop on Reliable Engineering Computing
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Vořechovský, Miroslav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Neuveden
https://schema.org/isbn
  • 978-981-08-5118-7
http://localhost/t...ganizacniJednotka
  • 26110
is http://linked.open...avai/riv/vysledek of
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