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Found 6 papers, 3 papers with code
Permutation invariant Gaussian matrix models for financial correlation matrices
For this case, we construct the general permutation invariant Gaussian matrix model, which has 4 parameters characterised using the representation theory of symmetric groups.
Permutation invariant matrix statistics and computational language tasks
The Linguistic Matrix Theory programme introduced by Kartsaklis, Ramgoolam and Sadrzadeh is an approach to the statistics of matrices that are generated in type-driven distributional semantics, based on permutation invariant polynomial functions which are regarded as the key observables encoding the significant statistics.
Quarter-BPS states, multi-symmetric functions and set partitions
In the case $n \leq N$ ($n$ being the dimension of the composite operator) the construction is analytic, using multi-symmetric functions and $U(2)$ Clebsch-Gordan coefficients.
High Energy Physics - Theory
Gaussianity and typicality in matrix distributional semantics
Using the general 13-parameter permutation invariant Gaussian matrix models recently solved, we find, using a dataset of matrices constructed via standard techniques in distributional semantics, that the expectation values of a large class of cubic and quartic observables show high gaussianity at levels between 90 to 99 percent.
Permutation Invariant Gaussian Matrix Models
We express the expectation values of all the quadratic graph-basis invariants and a selection of cubic and quartic invariants in terms of the representation theoretic parameters of the model.
Linguistic Matrix Theory
We propose a Matrix Theory approach to this data, based on permutation symmetry along with Gaussian weights and their perturbations.
