10 citations · 20 across the 3 of their papers we have counts for
3 papers
Riemannian statistics meets random matrix theory: towards learning from high-dimensional covariance matrices
Salem Said, Simon Heuveline, Cyrus Mostajeran
Riemannian Gaussian distributions were initially introduced as basic building blocks for learning models which aim to capture the intrinsic structure of statistical populations of…
Gaussian distributions on Riemannian symmetric spaces, random matrices, and planar Feynman diagrams
Simon Heuveline, Salem Said, Cyrus Mostajeran
Gaussian distributions can be generalized from Euclidean space to a wide class of Riemannian manifolds. Gaussian distributions on manifolds are harder to make use of in application…
Gaussian distributions on Riemannian symmetric spaces in the large N limit
Simon Heuveline, Salem Said, Cyrus Mostajeran
We consider Gaussian distributions on certain Riemannian symmetric spaces. In contrast to the Euclidean case, it is challenging to compute the normalization factors of such distrib…