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We present an inference algorithm and connected Monte Carlo based estimation procedures for metric estimation from landmark configurations distributed according to the transition distribution of a Riemannian Brownian motion arising from the…

Computer Vision and Pattern Recognition · Computer Science 2017-06-01 Stefan Sommer , Alexis Arnaudon , Line Kuhnel , Sarang Joshi

In this paper we introduce three Markovian couplings of Brownian motions on smooth Riemannian manifolds without boundary which sit at the crossroad of two concepts. The first concept is the one of shy coupling put forward in…

Probability · Mathematics 2015-09-29 Mihai N. Pascu , Ionel Popescu

Random geometric graphs are random graph models defined on metric measure spaces. A random geometric graph is generated by first sampling points from a metric space and then connecting each pair of sampled points independently with a…

Probability · Mathematics 2025-11-10 Han Huang , Pakawut Jiradilok , Elchanan Mossel

We present a study of the distance between a Brownian motion and a submanifold of a complete Riemannian manifold. We include a variety of results, including an inequality for the Laplacian of the distance function derived from a Jacobian…

Probability · Mathematics 2016-04-19 James Thompson

In this paper a compact Riemannian manifold with strictly convex boundary is reconstructed from its partial travel time data. This data assumes that an open measurement region on the boundary is given, and that for every point in the…

Differential Geometry · Mathematics 2022-04-20 Ella Pavlechko , Teemu Saksala

The problem of recovering the configuration of points from their partial pairwise distances, referred to as the Euclidean Distance Matrix Completion (EDMC) problem, arises in a broad range of applications, including sensor network…

Optimization and Control · Mathematics 2026-05-07 Chandler Smith , HanQin Cai , Abiy Tasissa

We specify the conditions when a manifold M embedded in an inner product space E is an invariant manifold of a stochastic differential equation (SDE) on E, linking it with the notion of second-order differential operators on M. When M is…

Probability · Mathematics 2024-06-06 Du Nguyen , Stefan Sommer

This paper presents a unified geometric framework for Brownian motion on manifolds, encompassing intrinsic Riemannian manifolds, embedded submanifolds, and Lie groups. The approach constructs the stochastic differential equation by…

Probability · Mathematics 2025-10-24 Taeyoung Lee , Gregory S. Chirikjian

We study random walks on sub-Riemannian manifolds using the framework of retractions, i.e., approximations of normal geodesics. We show that such walks converge to the correct horizontal Brownian motion if normal geodesics are approximated…

Probability · Mathematics 2023-11-30 Michael Herrmann , Pit Neumann , Simon Schwarz , Anja Sturm , Max Wardetzky

We consider reconstruction of a manifold, or, invariant manifold learning, where a smooth Riemannian manifold $M$ is determined from intrinsic distances (that is, geodesic distances) of points in a discrete subset of $M$. In the studied…

Probability · Mathematics 2019-05-20 Charles Fefferman , Sergei Ivanov , Matti Lassas , Hariharan Narayanan

We show Riemannian geometry could be studied by identifying the tangent bundle of a Riemannian manifold $\mathcal{M}$ with a subbundle of the trivial bundle $\mathcal{M} \times \mathcal{E}$, obtained by embedding $\mathcal{M}$…

Differential Geometry · Mathematics 2021-05-05 Du Nguyen

I prove that every adapted Brownian bridge on a geodesically complete connected Riemannian manifold is a semimartingale including its terminal time, without any further assumptions on the geometry. In particular, it follows that every such…

Probability · Mathematics 2016-04-29 Batu Güneysu

We show that, by sampling a sufficiently large number of random points in a neighborhood of a compact submanifold M of a Riemannian manifold N, one can recover the topology of M with high confidence. This holds under the assumptions on the…

Differential Geometry · Mathematics 2025-12-30 Reza Mirzaie

We define the Ricci curvature of Markov chains on metric spaces as a local contraction coefficient of the random walk acting on the space of probability measures equipped with a Wasserstein transportation distance. For Brownian motion on a…

Probability · Mathematics 2007-07-30 Yann Ollivier

Index expectation curvature K(x) = E[i_f(x)] on a compact Riemannian 2d-manifold M is the expectation of Poincare-Hopf indices i_f(x) and so satisfies the Gauss-Bonnet relation that the interval of K over M is Euler characteristic X(M).…

Differential Geometry · Mathematics 2020-01-22 Oliver Knill

The infinite Brownian loop on a Riemannian manifold is the limit in distribution of the Brownian bridge of length $T$ around a fixed origin when $T \rightarrow +\infty$. The aim of this note is to study its long-time asymptotics on…

Analysis of PDEs · Mathematics 2023-01-25 Effie Papageorgiou

This paper introduces a probabilistic formulation for the isometric embedding of a Riemannian manifold $(M^n,g)$ into Euclidean space $\mathbb{R}^q$. Given $\alpha \in ]\tfrac{1}{2},1]$, we show that a $C^{1,\alpha}$ embedding $u: M \to…

Probability · Mathematics 2024-04-26 Dominik Inauen , Govind Menon

In this paper we show that, under some curvature assumptions the integral of distance function on a compact Riemannian manifold is bounded below by the product of diameter, volume and a constant only depending on the dimension.

Differential Geometry · Mathematics 2019-02-15 Jianming Wan

We prove that knowing the length of geodesics joining points on the boundary of a two-dimensional, compact, simple Riemannian manifold with boundary, we can determine uniquely the Riemannian metric up to the natural obstruction.

Analysis of PDEs · Mathematics 2007-05-23 L. Pestov , G. Uhlmann

We consider a certain class of Riemannian submersions $\pi : N \to M$ and study lifted geodesic random walks from the base manifold $M$ to the total manifold $N$. Under appropriate conditions on the distribution of the speed of the geodesic…

Probability · Mathematics 2023-10-03 Jonathan Junné , Frank Redig , Rik Versendaal
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