Krengel–Lin decomposition for probability measures on hypergroups

Ergodic sequences of probability measures on commutative hypergroups

We study conditions on a sequence of probability measures {µ n } n on a commutative hyper-group K, which ensure that, for any representation π of K on a Hilbert space Ᏼ π and for any ξ ∈ Ᏼ π , (K π x (ξ)dµ n (x)) n converges to a π-invariant member of Ᏼ π. 1. Introduction. The mean ergodic theorem was originally formulated by von Neu-mann [13] for one-parameter unitary groups in Hilbert space. ...

on semihypergroups and hypergroups

in this thesis, first the notion of weak mutual associativity (w.m.a.) and the necessary and sufficient condition for a $(l,gamma)$-associated hypersemigroup $(h, ast)$ derived from some family of $lesssim$-preordered semigroups to be a hypergroup, are given. second, by proving the fact that the concrete categories, semihypergroups and hypergroups have not free objects we will introduce t...

Gaussian Approximations for Probability Measures on R

This paper concerns the approximation of probability measures on Rd with respect to the Kullback-Leibler divergence. Given an admissible target measure, we show the existence of the best approximation, with respect to this divergence, from certain sets of Gaussian measures and Gaussian mixtures. The asymptotic behavior of such best approximations is then studied in the frequently occuring small...

Geometric Inference for Probability Measures

Data often comes in the form of a point cloud sampled from an unknown compact subset of Euclidean space. The general goal of geometric inference is then to recover geometric and topological features (e.g., Betti numbers, normals) of this subset from the approximating point cloud data. It appears that the study of distance functions allows to address many of these questions successfully. However...

On weak Γ-(semi)hypergroups