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Neural networks (NNs) are known for their high predictive accuracy in complex learning problems. Beside practical advantages, NNs also indicate favourable theoretical properties such as universal approximation (UA) theorems. Binarized…

Machine Learning · Computer Science 2021-02-05 Mikail Yayla , Mario Günzel , Burim Ramosaj , Jian-Jia Chen

In this paper we study the asymptotic behaviour of weighted random sums when the sum process converges stably in law to a Brownian motion and the weight process has continuous trajectories, more regular than that of a Brownian motion. We…

Probability · Mathematics 2014-02-07 José Manuel Corcuera , David Nualart , Mark Podolskij

We ask if it is possible to find some particular continuous paths of unit length in linear Brownian motion. Beginning with a discrete version of the problem, we derive the asymptotics of the expected waiting time for several interesting…

Probability · Mathematics 2015-09-18 Jim Pitman , Wenpin Tang

The paper is devoted to discretization of integral norms of functions from a given finite dimensional subspace. Even though this problem is extremely important in applications, its systematic study has begun recently. In this paper we…

Classical Analysis and ODEs · Mathematics 2020-01-28 F. Dai , A. Prymak , A. Shadrin , V. Temlyakov , S. Tikhonov

We give an exact solution to the generalized Langevin equation of motion of a charged Brownian particle in a uniform magnetic field that is driven internally by an exponentially-correlated stochastic force. A strong dissipation regime is…

Statistical Mechanics · Physics 2008-02-13 Francis N. C. Paraan , Mikhail P. Solon , J. P. Esguerra

Assuming an effective quadratic Hamiltonian, we derive an approximate, linear stochastic equation of motion for the density-fluctuations in liquids, composed of overdamped Brownian particles. From this approach, time dependent two point…

Soft Condensed Matter · Physics 2017-04-26 Matthias Krüger , David S. Dean

We prove the existence and uniqueness of a strong solution of a stochastic differential equation with normal reflection representing the random motion of finitely many globules. Each globule is a sphere with time-dependent random radius and…

Probability · Mathematics 2010-02-16 Myriam Fradon

We describe generalized Brownian motion related to parabolic equation systems from a logical point of view, i.e., as a generalization of Anderson's random walk. The connection to classical spaces is based on the Loeb measure. It seems that…

Probability · Mathematics 2012-01-09 Joerg Kampen

In this note, we prove a sharp $L^p$-rate of convergence of the number of upcrossings to the local time of the Brownian motion. In particular, it provides novel $p$-variation estimates ($2 < p < \infty$) for the number of upcrossings of the…

Probability · Mathematics 2014-08-26 Alberto Ohashi , Alexandre B. Simas

We prove that any given function can be smoothly approximated by functions lying in the kernel of a linear operator involving at least one fractional component. The setting in which we work is very general, since it takes into account…

Analysis of PDEs · Mathematics 2018-10-22 Alessandro Carbotti , Serena Dipierro , Enrico Valdinoci

Let $A_t=\sum_{s\le t} F(X_{s-},X_s)$ be a purely discontinuous additive functional of a subordinate Brownian motion $X=(X_t, \mathbb P_x)$. We give a sufficient condition on the non-negative function $F$ that guarantees that finiteness of…

Probability · Mathematics 2017-06-16 Zoran Vondraček , Vanja Wagner

We prove that random walks on a family of tilings of d-dimensional Euclidean space, with a canonical choice of conductances, converge to Brownian motion modulo time parameterization. This class of tilings includes Delaunay triangulations…

Probability · Mathematics 2025-08-29 Ahmed Bou-Rabee , Ewain Gwynne

In this paper we give an explicit description of the universal unitary completion of certain locally Q_p-analytic representations of GL_2(F), where F is a finite extension of Q_p (this generalizes some results of Berger-Breuil for F=Q_p).…

Representation Theory · Mathematics 2012-04-02 Marco De Ieso

The universal approximation theorem, in one of its most general versions, says that if we consider only continuous activation functions $\sigma$, then a standard feedforward neural network with one hidden layer is able to approximate any…

Machine Learning · Computer Science 2020-02-18 Kai Fong Ernest Chong

In this paper, we present the asymptotic theory for integrated functions of increments of Brownian local times in space. Specifically, we determine their first-order limit, along with the asymptotic distribution of the fluctuations. Our key…

Probability · Mathematics 2023-11-03 Simon Campese , Nicolas Lengert , Mark Podolskij

In recent years, interest in approximation methods for stochastic differential equations (SDEs) with non-Lipschitz continuous coefficients has increased. We show lower bounds for the $L^p$-error of such methods in the case of approximation…

Probability · Mathematics 2025-05-02 Simon Ellinger

The paper considers a global version of the notion of log canonical threshold for plurisubharmonic functions $u$ of logarithmic growth in $\mathbb{C}^n$, aiming at description of the range of all $p>0$ such that $e^{-u}\in…

Complex Variables · Mathematics 2026-02-11 Carles Bivià-Ausina , Alexander Rashkovskii

We establish general conditions under which there exists uniform in time convergence between a stochastic process and its approximated system. These standardised conditions consist of a local in time estimate between the original and the…

Probability · Mathematics 2024-12-09 Katharina Schuh , Iain Souttar

We prove existence of infinite volume Gibbs measures relative to Brownian motion. We require the pair potential W to fulfill a uniform integrability condition, but otherwise our restrictions on the potentials are relatively weak. In…

Probability · Mathematics 2007-05-23 Volker Betz

In this paper, we develop a general approach to proving global and local uniform limit theorems for the Horvitz-Thompson empirical process arising from complex sampling designs. Global theorems such as Glivenko-Cantelli and Donsker…

Statistics Theory · Mathematics 2019-05-31 Qiyang Han , Jon A. Wellner
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