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In this paper, the distance correlation, which can measure the dependence between random vectors, is utilized for multivariate GSA for structural systems with multivariate output.
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Simulation of angles between random vectors was used to assess the significance of such correlations.
Simulations of angles between random vectors were used to assess the statistical significance of this correlation [ 42].
Two variants of the stochastic Bubnov Galerkin scheme are employed for computing the undetermined terms in the reduced basis representation, which arise from how the condition for orthogonality between two random vectors is interpreted.
Using this method, we found that the difference between NLM and random vectors is not statistically significant in any of the four configurations (we found p>0.3 in all of them).
The random vectors are selected from a multivariate normal distribution.
Consider a random vector being passed through a non-linear transform.
Fifty thousand simulations provided the following significance threshold for the absolute value of the inner product "R": probability that the observed R is lower than observed between random vectors: P<0.01, R = 0.651; P<0.001, R = 0.770.
Simulation of angles between random vectors (vectors of 14 dimensions with random components) was used to assess the significance of such correlations between vectors [28], [29].
Distance correlation is a new dependence coefficient that measures all types of dependence between random vectors X and Y in arbitrary dimension [63].
Distance correlation is a new dependence coefficient that measures all types of dependence between random vectors X and Y in arbitrary dimension [ 63].
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