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Separability is an important problem in theory of quantum entanglement. By using the Bloch representation of quantum states in terms of the Heisenberg-Weyl observable basis, we present a new separability criterion for bipartite quantum…

Quantum Physics · Physics 2020-02-04 Jingmei Chang , Meiyu Cui , Tinggui Zhang , Shao-Ming Fei

We derive necessary conditions for localization of continuous frames in terms of generalized Beurling densities. As an important application we provide necessary density conditions for sampling and interpolation in a very large class of…

Functional Analysis · Mathematics 2023-05-02 Mishko Mitkovski , Aaron Ramirez

Pathwise predictability of continuous time processes is studied in deterministic setting. We discuss uniform prediction in some weak sense with respect to certain classes of inputs. More precisely, we study possibility of approximation of…

Optimization and Control · Mathematics 2009-11-13 Nikolai Dokuchaev

Given additional distributional information in the form of moment restrictions, kernel density and distribution function estimators with implied generalised empirical likelihood probabilities as weights achieve a reduction in variance due…

Methodology · Statistics 2019-10-08 Vitaliy Oryshchenko , Richard J. Smith

We provide a general construction scheme for $\mathcal L^p$-strong Feller processes on locally compact separable metric spaces. Starting from a regular Dirichlet form and specified regularity assumptions, we construct an associated…

Functional Analysis · Mathematics 2013-06-26 Benedict Baur , Martin Grothaus , Patrik Stilgenbauer

We consider the determinantal point process with the confluent hypergeometric kernel. This process is a universal point process in random matrix theory and describes the distribution of eigenvalues of large random Hermitian matrices near…

Mathematical Physics · Physics 2024-02-20 Shuai-Xia Xu , Shu-Quan Zhao , Yu-Qiu Zhao

In this paper, an adaptive non-parametric method is proposed to estimate the scalar-valued nonlinear function that appears in uncertain systems governed by ordinary differential equations (ODEs). By employing an infinite-dimensional…

Optimization and Control · Mathematics 2021-03-15 Jia Guo , Sai Tej Paruchuri , Andrew J. Kurdila

In this note, we present a version of Hoeffding's inequality in a continuous-time setting, where the data stream comes from a uniformly ergodic diffusion process. Similar to the well-studied case of Hoeffding's inequality for discrete-time…

Probability · Mathematics 2019-03-26 Michael C. H. Choi , Evelyn Li

We study the density estimation problem with observations generated by certain dynamical systems that admit a unique underlying invariant Lebesgue density. Observations drawn from dynamical systems are not independent and moreover, usual…

Machine Learning · Statistics 2016-07-14 Hanyuan Hang , Ingo Steinwart , Yunlong Feng , Johan A. K. Suykens

We develop and implement new probabilistic strategy for proving basic results about long time behaviour for interacting diffusion processes on unbounded lattice. The concept of the solution used is rather weak as we construct the process as…

Probability · Mathematics 2016-11-08 Frantisek Zak

We derive a new discretisation method for first order PDEs of arbitrary spatial dimension, which is based upon a meshfree spatial approximation. This spatial approximation is similar to the SPH (smoothed particle hydrodynamics) technique…

Numerical Analysis · Mathematics 2016-01-25 Tobias Ramming , Holger Wendland

We consider diffusion processes in Hilbert spaces with constant non-degenerate diffusion operators and show that, under broad assumptions on the drift, the transition probabilities of the process are positive on ellipsoids associated with…

Probability · Mathematics 2016-02-09 Oxana Manita

State representations summarize our knowledge about a system. When unobservable quantities are introduced the state representation is typically no longer unique. However, this non-uniqueness does not affect subsequent inferences based on…

Quantum Physics · Physics 2009-11-10 Kae Nemoto , Samuel L. Braunstein

We address data-driven learning of the infinitesimal generator of stochastic diffusion processes, essential for understanding numerical simulations of natural and physical systems. The unbounded nature of the generator poses significant…

Machine Learning · Statistics 2024-05-22 Vladimir R. Kostic , Karim Lounici , Helene Halconruy , Timothee Devergne , Massimiliano Pontil

Among all generalized Ornstein-Uhlenbeck processes which sample the same invariant measure and for which the same amount of randomness (a $N$-dimensional Brownian motion) is injected in the system, we prove that the asymptotic rate of…

Probability · Mathematics 2021-10-07 Arnaud Guillin , Pierre Monmarché

We consider the discrete-time filtering problem in scenarios where the observation noise is low or degenerate. We focus on the case where the observation equation is a linear function of the state and the data involve additive noise.…

Computation · Statistics 2026-04-01 Abylay Zhumekenov , Alexandros Beskos , Dan Crisan , Ajay Jasra , Nikolas Kantas

We consider a system of semilinear partial differential equations (PDEs) with a nonlinearity depending on both the solution and its gradient. The Neumann boundary condition depends on the solution in a nonlinear manner. The uniform…

Probability · Mathematics 2022-01-14 Khaled Bahlali , Brahim Boufoussi , Soufiane Mouchtabih

We consider a class of non-trivial perturbations ${\mathscr A}$ of the degenerate Ornstein-Uhlenbeck operator in ${\mathbb R}^N$. In fact we perturb both the diffusion and the drift part of the operator (say $Q$ and $B$) allowing the…

Analysis of PDEs · Mathematics 2008-03-05 B. Farkas , L. Lorenzi

For the spectral fractional diffusion operator of order $2s\in (0,2)$ in bounded, curvilinear polygonal domains $\Omega$ we prove exponential convergence of two classes of $hp$ discretizations under the assumption of analytic data, without…

Numerical Analysis · Mathematics 2024-07-25 Lehel Banjai , Jens M. Melenk , Christoph Schwab

It is common, in deconvolution problems, to assume that the measurement errors are identically distributed. In many real-life applications, however, this condition is not satisfied and the deconvolution estimators developed for…

Statistics Theory · Mathematics 2008-12-18 Aurore Delaigle , Alexander Meister
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