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In this paper, we introduce a class of processes that contains many natural examples. The interesting feature of such type processes lays on its infinite memory that allows it to record a quite ancient history. Then, using the martingale…

Probability · Mathematics 2025-03-04 Paul Doukhan , Xiequan Fan

This paper shows how the theory of dynamic risk measures provides viscosity solutions to a family of second-order parabolic partial differential equations, even in the degenerate case. First, motivated by the martingale problem approach of…

Probability · Mathematics 2012-07-10 Jocelyne Bion-Nadal

In this paper, we derive comparison results for terminal values of $d$-dimensional special semimartingales and also for finite-dimensional distributions of multivariate L\'{e}vy processes. The comparison is with respect to nondecreasing,…

Probability · Mathematics 2016-08-14 Jan Bergenthum , Ludger Rüschendorf

We establish novel and general high-dimensional concentration inequalities and Berry-Esseen bounds for vector-valued martingales induced by Markov chains. We apply these results to analyze the performance of the Temporal Difference (TD)…

Machine Learning · Statistics 2026-05-22 Weichen Wu , Yuting Wei , Alessandro Rinaldo

Let $(\mathbb{P}^{s,x})_{(s,x)\in[0,T]\times E}$ be a family of probability measures, where $E$ is a Polish space,defined on the canonical probability space ${\mathbb D}([0,T],E)$ of $E$-valued cadlag functions. We suppose that a martingale…

Probability · Mathematics 2021-05-11 Adrien Barrasso , Francesco Russo

In this paper we consider the problem of computing the stationary distribution of nearly completely decomposable Markov processes, a well-established area in the classical theory of Markov processes with broad applications in the design,…

Numerical Analysis · Mathematics 2025-06-19 Vasileios Kalantzis , Mark S. Squillante , Chai Wah Wu

We establish subgeometric bounds on convergence rate of general Markov processes in the Wasserstein metric. In the discrete time setting we prove that the Lyapunov drift condition and the existence of a "good" $d$-small set imply…

Probability · Mathematics 2014-03-20 Oleg Butkovsky

The generalized Langevin equation is used as a model for various coarse-grained physical processes, e.g., the time evolution of the velocity of a given larger particle in an implicitly represented solvent, when the relevant time scales of…

Statistical Mechanics · Physics 2025-11-13 Niklas Bockius , Maximilian Braun , Kay Hofmann , Friederike Schmid , Martin Hanke

We analyze the Markov property of solutions to the compressible Navier--Stokes system perturbed by a general multiplicative stochastic forcing. We show the existence of an almost sure Markov selection to the associated martingale problem.…

Probability · Mathematics 2018-09-20 Dominic Breit , Eduard Feireisl , Martina Hofmanova

The stochastic properties of a Langevin-type Markov process can be extracted from a given time series by a Markov analysis. Also processes that obey a stochastically forced second order differential equation can be analyzed this way by…

Data Analysis, Statistics and Probability · Physics 2014-12-09 Bernd Lehle , Joachim Peinke

The application of Stochastic Differential Equations (SDEs) to the analysis of temporal data has attracted increasing attention, due to their ability to describe complex dynamics with physically interpretable equations. In this paper, we…

Machine Learning · Statistics 2017-08-09 Constantino A. García , Abraham Otero , Paulo Félix , Jesús Presedo , David G. Márquez

We consider the problem of `discrete-time persistence', which deals with the zero-crossings of a continuous stochastic process, X(T), measured at discrete times, T = n(\Delta T). For a Gaussian Stationary Process the persistence (no…

Statistical Mechanics · Physics 2009-11-07 George C. M. A. Ehrhardt , Alan J. Bray , Satya N. Majumdar

We use the two-time scale subordination in order to describe dynamical processes in continuous media with a long-term memory. Our consideration touches two physical examples in detail. First we study a temporal evolution of the species…

Statistical Mechanics · Physics 2011-11-15 Aleksander Stanislavsky , Karina Weron

Explicit and exact results are obtained for the joint queue-length distribution for the two-level non-preemptive Markovian priority queue. Marginal distributions are derived for the general multi-level problem. The results are based on a…

Probability · Mathematics 2023-12-08 Josef Zuk , David Kirszenblat

This paper is devoted to studying the averaging principle for stochastic differential equations with slow and fast time-scales, where the drift coefficients satisfy local Lipschitz conditions with respect to the slow and fast variables, and…

Probability · Mathematics 2020-08-19 Wei Liu , Michael Röckner , Xiaobin Sun , Yingchao Xie

We discuss a class of Backward Stochastic Differential Equations(BSDEs) with no driving martingale. When the randomness of the driver depends on a general Markov process $X$, those BSDEs are denominated Markovian BSDEs and can be associated…

Probability · Mathematics 2017-12-29 Adrien Barrasso , Francesco Russo

A variational formula for the asymptotic variance of general Markov processes is obtained. As application, we get a upper bound of the mean exit time of reversible Markov processes, and some comparison theorems between the reversible and…

Probability · Mathematics 2021-06-02 Lu-Jing Huang , Yong-Hua Mao , Tao Wang

We study normal approximations for a class of discrete-time occupancy processes, namely, Markov chains with transition kernels of product Bernoulli form. This class encompasses numerous models which appear in the complex networks…

Probability · Mathematics 2018-11-13 Liam Hodgkinson , Ross McVinish , Philip K. Pollett

We show the existence of superprocesses in a random medium with location dependent branching. Technically, we make use of a duality relation to establish the uniqueness of the martingale problem and to obtain the moment formulas.

Probability · Mathematics 2016-03-11 Congzao Dong

Digital memcomputing machines (DMMs) are a new class of computing machines that employ non-quantum dynamical systems with memory to solve combinatorial optimization problems. Here, we show that the time to solution (TTS) of DMMs follows an…

Emerging Technologies · Computer Science 2023-09-11 Daniel Primosch , Yuan-Hang Zhang , Massimiliano Di Ventra