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The ability to characterize the state of dynamic systems has been a pertinent task in the time series analysis community. Traditional measures such as Lyapunov exponents are often times difficult to recover from noisy data, especially if…

Signal Processing · Electrical Eng. & Systems 2020-12-21 Joshua Tempelman , Audun Myers , Jeffrey Scruggs , Firas Khasawneh

In this work we investigate the generic properties of a stochastic linear model in the regime of high-dimensionality. We consider in particular the Vector AutoRegressive model (VAR) and the multivariate Hawkes process. We analyze both…

Statistical Mechanics · Physics 2015-06-11 Iacopo Mastromatteo , Emmanuel Bacry , Jean-François Muzy

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve…

This paper presents a probabilistic model for reasoning about the state of a system as it changes over time, both due to exogenous and endogenous influences. Our target domain is a class of medical prediction problems that are neither so…

Artificial Intelligence · Computer Science 2013-02-21 Steve Hanks , David Madigan , Jonathan Gavrin

We consider the statics and dynamics of a single particle trapped in a one-dimensional harmonic potential, and subjected to a driving noise with memory, that is represented by a resetting stochastic process. The finite memory of this…

Statistical Mechanics · Physics 2024-01-18 Mathis Gueneau , Satya N. Majumdar , Gregory Schehr

We consider one-dimensional stochastic differential equations with a boundary condition, driven by a Poisson process. We study existence and uniqueness of solutions and the absolute continuity of the law of the solution. In the case when…

Probability · Mathematics 2007-05-23 Aureli Alabert , Miguel A. Marmolejo

Many studies on biological and soft matter systems report the joint presence of a linear mean-squared displacement and a non-Gaussian probability density exhibiting, for instance, exponential or stretched-Gaussian tails. This phenomenon is…

Statistical Mechanics · Physics 2019-07-24 Jakub Ślęzak , Krzysztof Burnecki , Ralf Metzler

The horizontal dynamics of a bouncing ball interacting with an irregular surface is investigated and is found to demonstrate behavior analogous to a random walk. Its stochastic character is substantiated by the calculation of a permutation…

Physics Education · Physics 2025-09-15 Luiz Antonio Barreiro

We consider the general class of time-homogeneous stochastic dynamical systems, both discrete and continuous, and study the problem of learning a representation of the state that faithfully captures its dynamics. This is instrumental to…

Machine Learning · Computer Science 2024-03-15 Vladimir R. Kostic , Pietro Novelli , Riccardo Grazzi , Karim Lounici , Massimiliano Pontil

We derive an integration by parts formula for functionals of determinantal processes on compact sets, completing the arguments of [4]. This is used to show the existence of a configuration-valued diffusion process which is non-colliding and…

Probability · Mathematics 2015-09-30 Laurent Decreusefond , Ian Flint , Nicolas Privault , Giovanni Luca Torrisi

We consider a class of stochastic dynamical systems, called piecewise deterministic Markov processes, with states $(x, \s)\in \O\times \G$, $\O$ being a region in $\bbR^d$ or the $d$--dimensional torus, $\G$ being a finite set. The…

Statistical Mechanics · Physics 2009-02-25 Alessandra Faggionato , Davide Gabrielli , Marco Ribezzi Crivellari

Dynamic heterogeneity has often been modeled by assuming that a single-particle observable, fluctuating at a molecular scale, is influenced by its coupling to environmental variables fluctuating on a second, perhaps slower, time scale.…

Condensed Matter · Physics 2009-11-07 Gregor Diezemann , Gerald Hinze , Hans Sillescu

A stochastic differential equation with coefficients defined in a scale of Hilbert spaces is considered. The existence and uniqueness of finite time solutions is proved by an extension of the Ovsyannikov method. This result is applied to a…

Functional Analysis · Mathematics 2018-05-15 Alexei Daletskii

The concept of stochastic Lagrangian and its use in statistical dynamics is illustrated theoretically, and with some examples. Dynamical variables undergoing stochastic differential equations are stochastic processes themselves, and their…

Statistical Mechanics · Physics 2020-03-18 Massimo Materassi

Port-Hamiltonian (pH) systems have been studied extensively for linear continuous-time dynamical systems. This manuscript presents a discrete-time pH descriptor formulation for linear, completely causal, scattering passive dynamical systems…

Optimization and Control · Mathematics 2023-10-18 Karim Cherifi , Hannes Gernandt , Dorothea Hinsen , Volker Mehrmann

The combined influence of oscillatory excitations and multiplicative stochastic perturbations of white noise type on isochronous systems in the plane is investigated. It is assumed that the intensity of perturbations decays with time and…

Dynamical Systems · Mathematics 2025-05-01 Oskar A. Sultanov

We introduce diffusively coupled networks where the dynamical system at each vertex is planar Hamiltonian. The problems we address are synchronisation and an analogue of diffusion-driven Turing instability for time-dependent homogeneous…

Chaotic Dynamics · Physics 2017-06-07 David S. Tourigny

We study the long time behavior (homogenization) of a diffusion in random medium with time and space dependent coefficients. The diffusion coefficient may degenerate. In Stochastic Process. Appl. (2007) (to appear), an invariance principle…

Probability · Mathematics 2008-08-26 Rémi Rhodes

We demonstrate the phenomenon of stochastic resonance (SR) for discrete-time dynamical systems. We investigate various systems that are not necessarily bistable, but do have two well defined states, switching between which is aided by…

chao-dyn · Physics 2009-10-30 Prashant M. Gade , Renuka Rai , Harjinder Singh

Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events. However, we usually do not have the equation of motion describing the flows, or how they…

Machine Learning · Computer Science 2020-01-09 Junteng Jia , Austin R. Benson
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