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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.
Fifty thousand simulations of the correlation between two random vectors of 14 elements were performed.
The term cross-covariance refers to the covariance cov X,Y) between two random vectors X and Y.
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The expectation of the inner-space vector product of two random vectors with expectation 0 is the trace of their co-variance matrix.
In February 2015, Maxeler has developed a dataflow implementation of the statistical method to analyse the relationship between two random variables, by calculating sums of vectors given as streams (application icon is given in Fig. 8). Figure 9 depicts calculating the sum of a moving window of elements by adding a new element and substituting the old one from the previous sum.
But then, there are also measures of co-movement between two random variables.
To this end, we generated synthetic triplets by creating two random 0/1 vectors and a third vector that matches one of the logical combinations of the pair.
The correlation co-efficient provides the measure of the strength of two random variables (vectors).
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).
First, we summarize the asymptotic orthogonality between random vectors.
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].
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