Generalized F‐discrepancy‐based point selection strategy for dependent random variables in uncertainty quantification of nonlinear structures

2019 ◽  
Vol 121 (7) ◽  
pp. 1507-1529 ◽  
Author(s):  
Junyi Yang ◽  
Jinju Tao ◽  
Bruno Sudret ◽  
Jianbing Chen
Keyword(s):  
Uncertainty Quantification ◽  
Random Variables ◽  
Selection Strategy ◽  
Nonlinear Structures ◽  
Point Selection ◽  
Dependent Random Variables

scholarly journals Uncertainty quantification for functional dependent random variables

2016 ◽  
Vol 32 (2) ◽  
pp. 559-583 ◽  
Author(s):  
Simon Nanty ◽  
Céline Helbert ◽  
Amandine Marrel ◽  
Nadia Pérot ◽  
Clémentine Prieur
Keyword(s):  
Uncertainty Quantification ◽  
Random Variables ◽  
Dependent Random Variables

scholarly journals On Some Conditions for Strong Law of Large Numbers for Weighted Sums of END Random Variables under Sublinear Expectations

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Xiaochen Ma ◽  
Qunying Wu
Keyword(s):  
Probability Space ◽  
Law Of Large Numbers ◽  
Random Variables ◽  
Weighted Sums ◽  
Dependent Random Variables ◽  
Strong Law ◽  
Sublinear Expectation ◽  
Negatively Dependent Random Variables ◽  
Large Numbers ◽  
End Random Variables

In this article, we research some conditions for strong law of large numbers (SLLNs) for weighted sums of extended negatively dependent (END) random variables under sublinear expectation space. Our consequences contain the Kolmogorov strong law of large numbers and the Marcinkiewicz strong law of large numbers for weighted sums of extended negatively dependent random variables. Furthermore, our results extend strong law of large numbers for some sequences of random variables from the traditional probability space to the sublinear expectation space context.

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