Monte carlo simulation for correlated variables with marginal distributions

Che Hao Chang, Yeou-Koung Tung, Jinn Chuang Yang

Research output: Contribution to journalArticle

77 Scopus citations

Abstract

As computation speed increases, Monte Carlo simulation is becoming a viable tool for engineering design and analysis. However, restrictions are often imposed on multivariate cases in which the involved stochastic parameters are correlated. In multivariate Monte Carlo simulation, a joint probability distribution is required that can only be derived for some limited cases. This paper proposes a practical multivariate Monte Carlo simulation that preserves the marginal distributions of random variables and their correlation structure without requiring the complete joint distribution. For illustration, the procedure is applied to the reliability analysis of a bridge pier against scouring.

Original languageEnglish
Pages (from-to)313-331
Number of pages19
JournalJournal of Hydraulic Engineering
Volume120
Issue number3
DOIs
StatePublished - 1 Jan 1994

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