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Random Matrix Theory

2017
Random matrix theory deals with the study of matrix-valued random variables. It is conventionally considered that random matrix theory dates back to the work of Wishart in 1928 [1] on the properties of matrices of the type XX † with X ε ℂ N×n a random matrix with independent Gaussian entries with zero mean and equal variance.
Couillet, Romain, Debbah, Merouane
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Random Matrix Theory

2016
In this chapter the Gaussian random matrix ensembles are investigated. We determine their Green’s functions and show that for small energy differences a soft mode appears. As a consequence, the non-linear sigma-model is introduced and the level correlations are determined.
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Random Matrix Theory and Its Applications

Statistical Science, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Random-Matrix Theory

2001
A wealth of empirical and numerical evidence suggests universality for local fluctuations in quantum energy or quasi-energy spectra of systems that display global chaos in their classical phase spaces. Exceptions apart, all such Hamiltonian matrices of sufficiently large dimension yield the same spectral fluctuations provided they have the same group ...
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Random Matrix Theory

1992
Before about 1956, there was no systematic statistical theory of nuclear energy level structure. There was a shortage of close spacings in experimentally obtained energy levels which was generally dismissed as being due to instrumental resolution failings.
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RANDOM MATRIX THEORY AND FINANCIAL CORRELATIONS

International Journal of Theoretical and Applied Finance, 2000
We show that results from the theory of random matrices are potentially of great interest when trying to understand the statistical structure of the empirical correlation matrices appearing in the study of multivariate financial time series. We find a remarkable agreement between the theoretical prediction (based on the assumption that the correlation
Laloux, Laurent   +3 more
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Random Matrix Theory

2004
In this chapter, we will work not with \(\mathrm{GL}(n, \mathbb{C})\) but with its compact subgroup U(n). As in the previous chapters, we will consider elements of \(\mathcal{R}_{k}\) as generalized characters on S k . If \(\mathbf{f} \in \mathcal{R}_{k}\), then \(f ={ \mathrm{ch}}^{(n)}(\mathbf{f}) \in \varLambda _{k}^{(n)}\) is a symmetric polynomial
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The biofilm matrix: multitasking in a shared space

Nature Reviews Microbiology, 2022
Hans-Curt Flemming   +2 more
exaly  

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