Hierarchical Subset Latin Hypercube Sampling for correlated random vectors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F09%3APU86025" target="_blank" >RIV/00216305:26110/09:PU86025 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical Subset Latin Hypercube Sampling for correlated random vectors

Popis výsledku v původním jazyce

In this paper, we suggest principles of a novel simulation method for analyses of functions g(X) of a random vector X, suitable for 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., hen 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 qui

Název v anglickém jazyce

Hierarchical Subset Latin Hypercube Sampling for correlated random vectors

Popis výsledku anglicky

In this paper, we suggest principles of a novel simulation method for analyses of functions g(X) of a random vector X, suitable for 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., hen 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 qui

Druh

D - Stať ve sborníku

CEP obor

EC - Imunologie

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

First International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering

ISBN

978-1-905088-33-1

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

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Název nakladatele

Neuveden

Místo vydání

Funchal, Madeira, Portugal

Místo konání akce

Funchal. Madeira

Datum konání akce

1. 9. 2009

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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