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Recently, a general non-parametric probabilistic approach of model uncertainties for dynamical systems has been proposed using the random matrix theory.
Using the random matrix theory, we can define the joint distribution of nonzero eigenvalues of a Wishart matrix for min(n t,n r )=m[25].
Using the random matrix criterion of Horn (1965) [ 29], a full set of random answers (ranging over 0 – 11) were constructed for all 74 variables and all patients.
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Hughes [5] used the Random Matrix Theory (RMT) and stated an interesting conjecture on the moments of the Hardy -function and its derivatives at its zeros subject to the truth of Riemann's hypothesis when the zeros are simple.
If an adversary having a large database of biometric features of various users steals the biohash of a system user and knows his secret key, the adversary can compute biohashes of each biometric feature in the database using the random projection matrix of the user and create a table of biohashes and their corresponding feature vectors.
The clustering elements are selected from the gene expression database using the random values of an index matrix.
The gel microsphere matrix and the sintered microsphere matrix were designed using the random packing of poly(lactide-co-glycolide) microspheres to create a three-dimensional porous structure.
Variance-covariance matrices were compared using the random skewers method [ 82, 86], run through a Monte-Carlo simulation implemented by PopTools [ 58].
Recently, the random matrix theory is used to explore eigenmode of industrial production index fluctuation in dissipative structure [16].
The random matrix hits were used to generate a distribution from which an empirical p-value was calculated for each window.
It was observed that the random matrix theory can be used to quantify damping model uncertainty in some cases.
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