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Given a collection of multidimensional pairs $\{(X_i,Y_i):1 \leq i\leq n\}$, we study the problem of projecting the associated suitably smoothed empirical measure onto the space of martingale couplings (i.e. distributions satisfying…

Probability · Mathematics 2025-10-20 Jose Blanchet , Johannes Wiesel , Erica Zhang , Zhenyuan Zhang

Constrained quantization for a Borel probability measure refers to the idea of estimating a given probability by a discrete probability with a finite number of supporting points lying on a specific set. The specific set is known as the…

In a variety of applications it is important to extract information from a probability measure $\mu$ on an infinite dimensional space. Examples include the Bayesian approach to inverse problems and possibly conditioned) continuous time…

Probability · Mathematics 2016-06-02 Frank Pinski , Gideon Simpson , Andrew Stuart , Hendrik Weber

We obtain a general bound for the Wasserstein-2 distance in normal approximation for sums of locally dependent random variables. The proof is based on an asymptotic expansion for expectations of second-order differentiable functions of the…

Probability · Mathematics 2019-01-23 Xiao Fang

It is well known that the space of invariant probability measures for transitive sub-shifts of finite type is a Poulsen simplex. In this article we prove that in the non-compact setting, for a large family of transitive countable Markov…

Dynamical Systems · Mathematics 2021-08-16 Godofredo Iommi , Anibal Velozo

Using results relating the complexity of a two dimensional subshift to its periodicity, we obtain an application to the well-known conjecture of Furstenberg on a Borel probability measure on $[0,1)$ which is invariant under both $x\mapsto…

Dynamical Systems · Mathematics 2016-02-11 Van Cyr , Bryna Kra

The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in $[0,1]$. This distance plays a fundamental role in machine learning and signal…

Machine Learning · Computer Science 2018-07-02 Frank Nielsen , Ke Sun

For a partially ordered set $(S, \mathord\preceq)$, the order (monotone) dimension is the minimum cardinality of total orders (respectively, real-valued order monotone functions) on $S$ that characterize the order $\preceq$. In this paper…

Quantum Physics · Physics 2022-11-11 Yui Kuramochi

We construct ergodic probability measures with infinite metric entropy for typical continuous maps and homeomorphisms on compact manifolds. We also construct sequences of such measures that converge to a zero-entropy measure.

Dynamical Systems · Mathematics 2025-04-15 Eleonora Catsigeras , Serge Troubetzkoy

We repurpose tools from the theory of quantitative rectifiability to study the qualitative rectifiability of measures in $\Bbb{R}^n$, $n\geq 2$. To each locally finite Borel measure $\mu$, we associate a function $\widetilde J_2(\mu, x)$…

Classical Analysis and ODEs · Mathematics 2015-07-01 Matthew Badger , Raanan Schul

We study the possibility of defining a distance on the whole space of measures, with the property that the distance between two measures having the same mass is the Wasserstein distance, up to a scaling factor. We prove that, under very…

Metric Geometry · Mathematics 2021-12-10 Luca Lombardini , Francesco Rossi

A stochastic algorithm is proposed, finding the set of generalized means associated to a probability measure on a compact Riemannian manifold M and a continuous cost function on the product of M by itself. Generalized means include p-means…

Probability · Mathematics 2013-05-28 Marc Arnaudon , Laurent Miclo

We show that the continuum hypothesis implies that every measure preserving near-action of a group on a standard Borel probability space $(X,\mu)$ has a pointwise implementation by Borel measure preserving automorphisms.

Logic · Mathematics 2009-10-04 Asger Tornquist

This paper derives new bounds on the difference of the entropies of two discrete random variables in terms of the local and total variation distances between their probability mass functions. The derivation of the bounds relies on maximal…

Information Theory · Computer Science 2016-11-17 Igal Sason

Probabilistic frames are a generalization of finite frames into the Wasserstein space of probability measures with finite second moment. We introduce new probabilistic definitions of duality, analysis, and synthesis and investigate their…

Functional Analysis · Mathematics 2017-05-03 Clare Wickman , Kasso Okoudjou

By a Cantor-like measure we mean the unique self-similar probability measure $\mu $ satisfying $\mu =\sum_{i=0}^{m-1}p_{i}\mu \circ S_{i}^{-1}$ where $% S_{i}(x)=\frac{x}{d}+\frac{i}{d}\cdot \frac{d-1}{m-1}$ for integers $2\leq d<m\le 2d-1$…

Metric Geometry · Mathematics 2018-10-02 Kathryn E. Hare , Kevin G. Hare , Brian P. M. Morris , Wanchun Shen

In number theory, great efforts have been undertaken to study the Cohen-Lenstra probability measure on the set of all finite abelian $p$-groups. On the other hand, group theorists have studied a probability measure on the set of all…

Number Theory · Mathematics 2009-12-31 Johannes Lengler

In this article, we study Wasserstein-type metrics and corresponding barycenters for mixtures of a chosen subset of probability measures called atoms hereafter. In particular, this works extends what was proposed by Delon and Desolneux [A…

Optimization and Control · Mathematics 2023-01-20 Geneviève Dusson , Virginie Ehrlacher , Nathalie Nouaime

We introduce and study a class of determinantal probability measures generalising the class of discrete determinantal point processes. These measures live on the Grassmannian of a real, complex, or quaternionic inner product space that is…

Probability · Mathematics 2023-08-22 Adrien Kassel , Thierry Lévy

We identify two sufficient conditions for locally finite Borel measures on $\mathbb{R}^n$ to give full mass to a countable family of Lipschitz images of $\mathbb{R}^m$. The first condition, extending a prior result of Pajot, is a sufficient…

Classical Analysis and ODEs · Mathematics 2020-07-21 Matthew Badger , Raanan Schul