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We study the stationary scattering theory for a perturbed 1-body Stark operator. We prove existence and completeness of the stationary wave operators, construct the associated generalized Fourier transforms, and characterize asymptotics of…

Mathematical Physics · Physics 2021-02-25 T. Adachi , K. Itakura , K. Ito , E. Skibsted

We consider a strictly substochastic matrix or an stochastic matrix with absorbing states. By using quasi-stationary distributions one shows there is a canonical associated stationary Markov chain. Based upon $2-$stringing representation of…

Probability · Mathematics 2019-10-03 Servet Martínez

The production of molecules in a chemical reaction network is modelled as a Poisson process with a Markov-modulated arrival rate and an exponential decay rate. We analyze the distributional properties of $M$, the number of molecules, under…

Probability · Mathematics 2013-09-17 D. Anderson , J. Blom , M. Mandjes , H. Thorsdottir , K. de Turck

Strong invariance principles in Markov chain Monte Carlo are crucial to theoretically grounded output analysis. Using the wide-sense regenerative nature of the process, we obtain explicit bounds in the strong invariance converging rates for…

Computation · Statistics 2025-04-11 Arka Banerjee , Dootika Vats

In the field of large deviations for stochastic dynamics, the canonical conditioning of a given Markov process with respect to a given time-local trajectory observable over a large time-window has attracted a lot of interest recently. In…

Statistical Mechanics · Physics 2024-01-24 Cecile Monthus

Stochastic processes with random reinforced relocations have been introduced in the physics literature to model animal foraging behaviour. Such a process evolves as a Markov process, except at random relocation times, when it chooses a time…

Probability · Mathematics 2023-07-12 Erion-Stelios Boci , Cécile Mailler

In many applications, the common assumption that a driving noise process affecting a system is independent or Markovian may not be realistic, but the noise process may be assumed to be stationary. To study such problems, this paper…

Probability · Mathematics 2018-01-08 Serdar Yüksel

This paper is devoted to establish an invariance principle where the limit process is a multifractional Gaussian process with a multifractional function which takes its values in $(1/2,1)$. Some properties, such as regularity and local…

Probability · Mathematics 2009-09-29 Serge Cohen , Renaud Marty

In this announcement we generalize the Markov-Kakutani fixed point theorem for abelian semi-groups of affine transformations extending it on some class of non-commutative semi-groups. As an interesting example we apply it obtaining a…

Functional Analysis · Mathematics 2007-05-23 Jaroslaw Wawrzycki

Consider an ergodic Markov chain on a countable state space for which the return times have exponential tails. We show that the stationary version of any such chain is a finitary factor of an i.i.d. process. A key step is to show that any…

Probability · Mathematics 2023-06-22 Omer Angel , Yinon Spinka

Strong invariance principles describe the error term of a Brownian approximation of the partial sums of a stochastic process. While these strong approximation results have many applications, the results for continuous-time settings have…

Statistics Theory · Mathematics 2022-06-17 Ardjen Pengel , Joris Bierkens

We obtain quenched almost sure invariance principle (with convergence rate) for Random Young Tower. We apply our result to i.i.d perturbations of non-uniformly expanding maps. In particular, we answer one open question in \cite{BBM}.

Dynamical Systems · Mathematics 2020-01-22 Yaofeng Su

A general criterion is given for when a Markov chain trapped with probability p(x) in state x will be almost surely trapped. The quenched (state x is a trap forever with probability p(x)) and annealed (state x traps with probability p(x) on…

Probability · Mathematics 2016-09-07 Robin Pemantle , Stanislav Volkov

We consider a renewal process that is conditioned on the number of events in a fixed time horizon. We prove that a centered and scaled version of this process converges to a Brownian bridge, as the number of events grows large, which relies…

Probability · Mathematics 2017-11-08 Harsha Honnappa , Rahul Jain , Amy R. Ward

Computational procedures for the stationary probability distribution, the group inverse of the Markovian kernel and the mean first passage times of an irreducible Markov chain, are developed using perturbations. The derivation of these…

Probability · Mathematics 2016-10-12 Jeffrey J. Hunter

We establish almost sure invariance principles, a strong form of approximation by Brownian motion, for non-stationary time-series arising as observations on dynamical systems. Our examples include observations on sequential expanding maps,…

Dynamical Systems · Mathematics 2014-06-18 N. Haydn , M. Nicol , A. Tôrôk , S. Vaienti

We consider the piecewise-deterministic Markov process obtained by randomly switching between the flows generated by a finite set of smooth vector fields on a compact set. We obtain H\"ormander-type conditions on the vector fields…

Probability · Mathematics 2023-02-14 Michel Benaïm , Oliver Tough

Based on a weak convergence argument, we provide a necessary and sufficient condition that guarantees that a nonnegative local martingale is indeed a martingale. Typically, conditions of this sort are expressed in terms of integrability…

Probability · Mathematics 2014-04-24 Jose Blanchet , Johannes Ruf

For each $n$ let $Y^n_t$ be a continuous time symmetric Markov chain with state space $n^{-1} \Z^d$. A condition in terms of the conductances is given for the convergence of the $Y^n_t$ to a symmetric Markov process $Y_t$ on $\R^d$. We have…

Probability · Mathematics 2008-07-22 R. F. Bass , T. Kumagai , T. Uemura

For a class of irreducible Markov chains with an infinitely countable set of states, we establish a new verifiable necessary and sufficient condition for recurrence and transience. We show that if one of the basic assumptions is not…

Probability · Mathematics 2024-10-08 Vyacheslav M. Abramov
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