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Statements

Subject Item
n2:RIV%2F00216305%3A26110%2F10%3APU91705%21RIV11-GA0-26110___
rdf:type
skos:Concept n9:Vysledek
dcterms:description
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
dcterms:title
Extension of sample size in Latin Hypercube Sampling with correlated variables Extension of sample size in Latin Hypercube Sampling with correlated variables
skos:prefLabel
Extension of sample size in Latin Hypercube Sampling with correlated variables Extension of sample size in Latin Hypercube Sampling with correlated variables
skos:notation
RIV/00216305:26110/10:PU91705!RIV11-GA0-26110___
n3:aktivita
n14:P
n3:aktivity
P(GA103/08/0752), P(KJB201720902)
n3:dodaniDat
n7:2011
n3:domaciTvurceVysledku
n13:1352687
n3:druhVysledku
n19:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
258548
n3:idVysledku
RIV/00216305:26110/10:PU91705
n3:jazykVysledku
n21:eng
n3:klicovaSlova
Latin Hypercube Sampling
n3:klicoveSlovo
n15:Latin%20Hypercube%20Sampling
n3:kontrolniKodProRIV
[EA17EF1AA3EB]
n3:mistoKonaniAkce
Singapur
n3:mistoVydani
Neuveden
n3:nazevZdroje
proc. of 4th International Workshop on Reliable Engineering Computing
n3:obor
n16:JN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n12:GA103%2F08%2F0752 n12:KJB201720902
n3:rokUplatneniVysledku
n7:2010
n3:tvurceVysledku
Vořechovský, Miroslav
n3:typAkce
n6:WRD
n3:zahajeniAkce
2010-03-03+01:00
s:numberOfPages
16
n17:hasPublisher
Neuveden
n4:isbn
978-981-08-5118-7
n10:organizacniJednotka
26110