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Stochastic evolution equations with compensated Poisson noise are considered in the variational approach with monotone and coercive coefficients. Here the Poisson noise is assumed to be time-homogeneous with $\sigma$-finite intensity…

Probability · Mathematics 2022-04-20 Sima Mehri , Erfan Salavati , Bijan Z. Zangeneh

We present a novel methodology based on a Taylor expansion of the network output for obtaining analytical expressions for the expected value of the network weights and output under stochastic training. Using these analytical expressions the…

Machine Learning · Statistics 2019-12-19 Anastasia Borovykh

Trace-wise noise is a type of noise often seen in seismic data, which is characterized by vertical coherency and horizontal incoherency. Using self-supervised deep learning to attenuate this type of noise, the conventional blind-trace deep…

Geophysics · Physics 2024-04-04 Mohammad Mahdi Abedi , David Pardo , Tariq Alkhalifah

In this paper, we establish large deviation principle for the strong solution of evolutionary p-Laplace equation driven by small multiplicative Brownian noise, where the weak convergence approach plays a key role. Moreover, by using…

Probability · Mathematics 2022-10-21 Kavin R , Ananta K Majee

We give a graded version of the M\"obius inversion formula in the framework of trace monoids. The formula is based on a graded version of the M\"obius transform, related to the notion of height deriving from the Cartier-Foata normal form of…

Combinatorics · Mathematics 2015-05-05 Samy Abbes

We consider statistics for stochastic evolution equations in Hilbert space with emphasis on stochastic partial differential equations (SPDEs). We observe a solution process under additional measurement errors and want to estimate a real or…

Statistics Theory · Mathematics 2025-05-21 Gregor Pasemann , Markus Reiß

This paper considers unconstrained convex optimization problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and correction…

Information Theory · Computer Science 2017-09-18 Andrea Simonetto , Aryan Mokhtari , Alec Koppel , Geert Leus , Alejandro Ribeiro

This paper specifies an observational semantics and gives an original presentation of the Byrd box model. The approach accounts for the semantics of Prolog tracers independently of a particular Prolog implementation. Prolog traces are, in…

Programming Languages · Computer Science 2007-11-27 Pierre Deransart , Mireille Ducassé , Gérard Ferrand

The solution of a parabolic stochastic partial differential equation (SPDE) driven by an infinite-dimensional Brownian motion is in general not a semi-martingale anymore and does in general not satisfy an It\^{o} formula like the solution…

Probability · Mathematics 2010-10-04 Arnulf Jentzen , Peter Kloeden

The Error-in-Variables model of system identification/control involves nontrivial input and measurement corruption of observed data, resulting in generically nonconvex optimization problems. This paper performs full-state-feedback…

Optimization and Control · Mathematics 2024-05-21 Jared Miller , Tianyu Dai , Mario Sznaier

Given integers $m$, $n$ and $k$, we give an explicit formula with an optimal error term (with square root cancelation) for the Petersson trace formula involving the $m$-th and $n$-th Fourier coefficients of an orthonormal basis of…

Number Theory · Mathematics 2020-08-04 Junehyuk Jung , Simon Marshall , Naser T. Sardari

We analyze the problem of evolution in a system with stochastic perturbation and point out that analytic properties of the noise present in the system might determine spectral properties of the evolution operator (Frobenius-Perron…

Chaotic Dynamics · Physics 2009-11-07 A. Ostruszka , K. Zyczkowski

This paper considers the problem of recovering the permutation of an n-dimensional random vector X observed in Gaussian noise. First, a general expression for the probability of error is derived when a linear decoder (i.e., linear estimator…

Information Theory · Computer Science 2021-05-10 Minoh Jeong , Alex Dytso , Martina Cardone

We present a new derivation of the equations governing the oscillations of slowly rotating relativistic stars. Previous investigations have been mostly carried out in the Regge-Wheeler gauge. However, in this gauge the process of…

General Relativity and Quantum Cosmology · Physics 2009-11-07 Johannes Ruoff , Adamantios Stavridis , Kostas D. Kokkotas

It is shown that a well-known theory of random stationary processes contain contradictions. Integral representations of correlation functions and random stationary processes are investigated further. The new method of struggle with…

Statistics Theory · Mathematics 2011-03-09 V. N. Tibabishev

This paper considers the problem of estimating a periodic function in a continuous time regression model with an additive stationary gaussian noise having unknown correlation function. A general model selection procedure on the basis of…

Statistics Theory · Mathematics 2010-11-10 Victor Konev , Serguei Pergamenchtchikov

We use an effective Markovian description to study the long-time behaviour of a nonlinear second order Langevin equation with Gaussian noise. When dissipation is neglected, the energy of the system grows as with time a power-law with an…

Chaotic Dynamics · Physics 2014-12-19 Kirone Mallick

This article is concerned with the second order necessary conditions for the stochastic optimal control problem of stochastic evolution equation with model uncertainty when the traditional Pontryagin-type maximum principle holds trivially…

Optimization and Control · Mathematics 2025-12-09 Guangdong Jing

In this work, we investigate the large-scale transport properties of a passive scalar advected by a turbulent fluid, modelled as a superposition of divergence-free vector fields, each weighted by an independent symmetric…

Mathematical Physics · Physics 2026-02-25 Paolo Cifani , Franco Flandoli , Lorenzo Marino

Using the $\ell_1$-norm to regularize the estimation of the parameter vector of a linear model leads to an unstable estimator when covariates are highly correlated. In this paper, we introduce a new penalty function which takes into account…

Machine Learning · Computer Science 2011-09-14 Edouard Grave , Guillaume Obozinski , Francis Bach