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We study curvature invariants of a sub-Riemannian manifold (i.e., a manifold with a Riemannian metric on a non-holonomic distribution) related to mutual curvature of several pairwise orthogonal subspaces of the distribution, and prove…

Differential Geometry · Mathematics 2022-12-27 Vladimir Rovenski

Wasserstein geometry and information geometry are two important structures to be introduced in a manifold of probability distributions. Wasserstein geometry is defined by using the transportation cost between two distributions, so it…

Statistics Theory · Mathematics 2021-01-01 Shun-ichi Amari , Takeru Matsuda

Probability distributions in Stiefel manifold such as the von-Mises Fisher and Bingham distributions find diverse applications in signal processing and other applied sciences. Use of these statistical models in practice is complicated by…

Statistics Theory · Mathematics 2016-02-15 Lu Wei , Jukka Corander

This paper investigates the failure of certain metric measure spaces to be infinitesimally Hilbertian or quasi-Riemannian manifolds, by constructing examples arising from a manifold $M$ endowed with a Riemannian metric $g$ that is possibly…

Differential Geometry · Mathematics 2026-03-31 Vanessa Ryborz

The dually flat structure of statistical manifolds can be derived in a non-parametric way from a particular case of affine space defined on a qualified set of probability measures. The statistically natural displacement mapping of the…

Statistics Theory · Mathematics 2022-10-17 Giovanni Pistone

A mathematical framework is developed for the analysis of causal fermion systems in the infinite-dimensional setting. It is shown that the regular spacetime point operators form a Banach manifold endowed with a canonical Fr\'echet-smooth…

Mathematical Physics · Physics 2021-07-29 Felix Finster , Magdalena Lottner

Given data, deep generative models, such as variational autoencoders (VAE) and generative adversarial networks (GAN), train a lower dimensional latent representation of the data space. The linear Euclidean geometry of data space pulls back…

Computer Vision and Pattern Recognition · Computer Science 2018-05-22 Line Kuhnel , Tom Fletcher , Sarang Joshi , Stefan Sommer

We review basic notions in the field of information geometry such as Fisher metric on statistical manifold, $\alpha$-connection and corresponding curvature following Amari's work . We show application of information geometry to asymptotic…

Statistics Theory · Mathematics 2014-10-14 Mashbat Suzuki

This paper is a short summary of our recent work on the medians and means of probability measures in Riemannian manifolds. Firstly, the existence and uniqueness results of local medians are given. In order to compute medians in practical…

Differential Geometry · Mathematics 2011-11-16 Marc Arnaudon , Frédéric Barbaresco , Le Yang

As objects of study in functional analysis, Hilbert spaces stand out as special objects of study as do nuclear spaces in view of a rich geometrical structure they possess as Banach and Frechet spaces, respectively. On the other hand, there…

Functional Analysis · Mathematics 2013-10-29 M A Sofi

We prove a Frobenius theorem for Banach distributions on manifolds that are modelled over locally convex spaces. Moreover, we recall how Frobenius theorems can be applied to infinite-dimensional Lie groups and obtain, that given a Lie…

Group Theory · Mathematics 2014-07-14 Jan Milan Eyni

This article develops nonparametric inference procedures for estimation and testing problems for means on manifolds. A central limit theorem for Frechet sample means is derived leading to an asymptotic distribution theory of intrinsic…

Statistics Theory · Mathematics 2007-06-13 Rabi Bhattacharya , Vic Patrangenaru

Let $\mathcal{M}$ be a smooth submanifold of $\mathbb{R}^n$ equipped with the Euclidean (chordal) metric. This note considers the smallest dimension $m$ for which there exists a bi-Lipschitz function $f: \mathcal{M} \mapsto \mathbb{R}^m$…

Numerical Analysis · Mathematics 2021-05-31 Mark Iwen , Arman Tavakoli , Benjamin Schmidt

Riemannian structures on infinite-dimensional manifolds arise naturally in shape analysis and shape optimization. These applications lead to optimization problems on manifolds which are not modeled on Banach spaces. The present article…

Optimization and Control · Mathematics 2026-04-21 Valentina Zalbertus , Max Pfeffer , Alexander Schmeding

We develop a theory of large scale geometry of metrisable topological groups that, in a significant number of cases, allows one to define and identify a unique quasi-isometry type intrinsic to the topological group. Moreover, this…

Group Theory · Mathematics 2014-03-14 Christian Rosendal

The notion of nonpositive curvature in Alexandrov's sense is extended to include p-uniformly convex Banach spaces. Infinite dimensional manifolds of semi-negative curvature with a p-uniformly convex tangent norm fall in this class on…

Differential Geometry · Mathematics 2014-02-26 Cristian Conde , Gabriel Larotonda

We consider an inference on the eigenvalues of the covariance matrix of a multivariate normal distribution. The family of multivariate normal distributions with a fixed mean is seen as a Riemannian manifold with Fisher information metric.…

Statistics Theory · Mathematics 2018-10-12 Yo Sheena

We show that smoothness implies norm-controlled inversion: the smoothness of an element $a$ in a Banach algebra with a one-parameter automorphism group is preserved under inversion, and the norm of the inverse $a^{-1}$ is controlled by the…

Functional Analysis · Mathematics 2014-07-17 Karlheinz Gröchenig , Andreas Klotz

In this paper we define a new type of quadratic variation for cylindrical continuous local martingales on an infinite dimensional spaces. It is shown that a large class of cylindrical continuous local martingales has such a quadratic…

Probability · Mathematics 2018-04-11 Mark Veraar , Ivan Yaroslavtsev

This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses. Such problems arise in various machine learning and signal processing applications. By…

Optimization and Control · Mathematics 2022-07-18 Jiang Hu , Ruicheng Ao , Anthony Man-Cho So , Minghan Yang , Zaiwen Wen
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