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This paper derives fundamental limits on the performance of compressive classification when the source is a mixture of Gaussians. It provides an asymptotic analysis of a Bhattacharya based upper bound on the misclassification probability…

Information Theory · Computer Science 2014-01-28 Hugo Reboredo , Francesco Renna , Robert Calderbank , Miguel R. D. Rodrigues

Fix a positive integer $d$ and let $(G_n)_{n\geq1}$ be a sequence of finite abelian groups with orders tending to infinity. For each $n \geq 1$, let $C_n$ be a uniformly random $G_n$-circulant matrix with entries in $\{0,1\}$ and exactly…

Probability · Mathematics 2025-04-21 Adrian Beker

We determine the order of magnitude of $\mathbb{E}|\sum_{n \leq x} f(n)|^{2q}$, where $f(n)$ is a Steinhaus or Rademacher random multiplicative function, and $0 \leq q \leq 1$. In the Steinhaus case, this is equivalent to determining the…

Number Theory · Mathematics 2017-03-21 Adam J. Harper

There is a result of Diaconis and Freedman which says that, in a limiting sense, for large collections of high-dimensional data most one-dimensional projections of the data are approximately Gaussian. This paper gives quantitative versions…

Probability · Mathematics 2010-05-18 Elizabeth Meckes

We consider random multimodal $C^3$ maps with negative Schwarzian derivative, defined on a finite union of closed intervals in $[0,1]$, onto the interval $[0,1]$ with the base space $\Omega$ and a base invertible ergodic map…

Dynamical Systems · Mathematics 2021-07-16 Jason Atnip , Mariusz Urbański

Let $\mu$ be a log-concave probability measure on ${\mathbb R}^n$ and for any $N>n$ consider the random polytope $K_N={\rm conv}\{X_1,\ldots ,X_N\}$, where $X_1,X_2,\ldots $ are independent random points in ${\mathbb R}^n$ distributed…

Probability · Mathematics 2023-09-18 Silouanos Brazitikos , Apostolos Giannopoulos , Minas Pafis

Building on recent results regarding symmetric probabilistic constructions of countable structures, we provide a method for constructing probability measures, concentrated on certain classes of countably infinite structures, that are…

Logic · Mathematics 2015-11-24 Nathanael Ackerman , Cameron Freer , Jaroslav Nesetril , Rehana Patel

In this manuscript, we consider the Langevin dynamics on $\mathbb{R}^d$ with an overdamped vector field and driven by multiplicative Brownian noise of small amplitude $\sqrt{\epsilon}$, $\epsilon>0$. Under suitable assumptions on the vector…

Probability · Mathematics 2023-05-05 Gerardo Barrera

Let $G$ be a locally compact group and $\mu$ be a probability measure on $G$. We consider the convolution operator $\lambda_1(\mu)\colon L_1(G)\to L_1(G)$ given by $\lambda_1(\mu)f=\mu \ast f$ and its restriction $\lambda_1^0(\mu)$ to the…

Functional Analysis · Mathematics 2023-12-14 Jorge Galindo , Enrique Jordá , Alberto Rodríguez-Arenas

One-dimensional maps exhibiting transient chaos and defined on two preimages of the unit interval [0,1] are investigated. It is shown that such maps have continuously many conditionally invariant measures $\mu_{\sigma}$ scaling at the fixed…

chao-dyn · Physics 2009-10-31 Z. Kaufmann , A. Németh , P. Szépfalusy

We show that any self-conformal measure $\mu$ on $\mathbb{R}$ is uniformly scaling and generates an ergodic fractal distribution. This generalizes existing results by removing the need for any separation condition. We also obtain…

Dynamical Systems · Mathematics 2023-08-23 Balázs Bárány , Antti Käenmäki , Aleksi Pyörälä , Meng Wu

We associate to every function $u\in GBD(\Omega)$ a measure $\mu_u$ with values in the space of symmetric matrices, which generalises the distributional symmetric gradient $Eu$ defined for functions of bounded deformation. We show that this…

Functional Analysis · Mathematics 2025-06-26 Gianni Dal Maso , Davide Donati

We study the perturbation of a measure $\mu \in \mathscr{P}(\mathbb{R})$ consisting in superposing two copies of $\mu$, each slightly shifted by a small distance $\pm h$. The difference between $\mu$ and its perturbation is measured with a…

Optimization and Control · Mathematics 2026-03-03 Averil Aussedat

The optimal quantum measurements for estimating different unknown parameters in a parameterized quantum state are usually incompatible with each other. Traditional approaches to addressing the measurement incompatibility issue, such as the…

Quantum Physics · Physics 2026-02-03 Sisi Zhou , Senrui Chen

Let $X$ be a proper geodesic Gromov hyperbolic metric space and let $G$ be a cocompact group of isometries of $X$ admitting a uniform lattice. Let $d$ be the Hausdorff dimension of the Gromov boundary $\partial X$. We define the critical…

Group Theory · Mathematics 2018-10-01 Ilya Gekhtman , Arie Levit

In this note we continue the study of imaginary multiplicative chaos $\mu_\beta := \exp(i \beta \Gamma)$, where $\Gamma$ is a two-dimensional continuum Gaussian free field. We concentrate here on the fine-scale analytic properties of…

Probability · Mathematics 2025-01-17 Juhan Aru , Guillaume Baverez , Antoine Jego , Janne Junnila

We study random Morse functions on a Riemann manifold $(M^m,g)$ defined as a random Gaussian weighted superpositions of eigenfunctions of the Laplacian of the metric $g$. The randomness is determined by a fixed Schwartz function $w$ and a…

Differential Geometry · Mathematics 2014-03-12 Liviu I. Nicolaescu

We study self-similar measures in $\mathbb{R}$ satisfying the weak separation condition along with weak technical assumptions which are satisfied in all known examples. For such a measure $\mu$, we show that there is a finite set of concave…

Dynamical Systems · Mathematics 2021-04-20 Alex Rutar

Product measures of dimension $n$ are known to be concentrated in Hamming distance: for any set $S$ in the product space of probability $\epsilon$, a random point in the space, with probability $1-\delta$, has a neighbor in $S$ that is…

Data Structures and Algorithms · Computer Science 2019-07-12 Omid Etesami , Saeed Mahloujifar , Mohammad Mahmoody

Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the finite-dimensional Gaussian approach from Maatouk and Bay (2017)…

Machine Learning · Statistics 2021-11-04 Andrés F. López-Lopera , François Bachoc , Nicolas Durrande , Olivier Roustant