95 citations · 155 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size
Alain Durmus, Pablo Jiménez, Éric Moulines +1
This paper studies fixed step-size stochastic approximation (SA) schemes, including stochastic gradient schemes, in a Riemannian framework. It is motivated by several applications,…
Online learning of Riemannian hidden Markov models in homogeneous Hadamard spaces
Quinten Tupker, Salem Said, Cyrus Mostajeran
Hidden Markov models with observations in a Euclidean space play an important role in signal and image processing. Previous work extending to models where observations lie in Riema…
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…
Statistical models and probabilistic methods on Riemannian manifolds
Salem Said
This entry contains the core material of my habilitation thesis, soon to be officially submitted. It provides a self-contained presentation of the original results in this thesis,…
Hidden Markov chains and fields with observations in Riemannian manifolds
Salem Said, Nicolas Le Bihan, Jonathan H. Manton
Hidden Markov chain, or Markov field, models, with observations in a Euclidean space, play a major role across signal and image processing. The present work provides a statistical…