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An infinite system of point particles placed in $\mathds{R}^d$ is studied. Its constituents perform random jumps with mutual repulsion described by a translation-invariant jump kernel and interaction potential, respectively. The pure states…

Probability · Mathematics 2021-03-18 Yuri Kozitsky , Michael Röckner

For an infinite system of particles arriving in and departing from a habitat $X$ -- a locally compact Polish space with a positive Radon measure $\chi$ -- a Markov process is constructed in an explicit way. Along with its location $x\in X$,…

Probability · Mathematics 2021-12-10 Dominika Jasinska , Yuri Kozitsky

An individual-based model of an infinite system of point particles in $\mathbb{R}^d$ is proposed and studied. In this model, each particle at random produces a finite number of new particles and disappears afterwards. The phase space for…

Dynamical Systems · Mathematics 2015-10-27 Agnieszka Tanaś

The totally asymmetric simple exclusion process (TASEP) is a basic model of statistical mechanics that has found numerous applications. We consider the case of TASEP with a finite chain where particles may enter from the left and leave to…

Dynamical Systems · Mathematics 2020-06-23 Lars Grüne , Thomas Kriecherbauer , Michael Margaliot

The notion of a successful coupling of Markov processes, based on the idea that both components of the coupled system ``intersect'' in finite time with probability one, is extended to cover situations when the coupling is unnecessarily…

Probability · Mathematics 2007-05-23 Michael Blank , Sergey Pirogov

The paper discusses a family of Markov processes that represent many particle systems, and their limiting behaviour when the number of particles go to infinity. The first part concerns model of biological systems: a model for sympatric…

Probability · Mathematics 2011-04-29 Bernt Wennberg

Discrete time random dynamical systems with countably many maps which admit countable Markov partitions on complete metric spaces such that the resulting Markov systems are uniform continuous and contractive are considered. A notion of a…

Probability · Mathematics 2015-06-16 Ivan Werner

It is well-known that well-posedness of a martingale problem in the class of continuous (or r.c.l.l.) solutions enables one to construct the associated transition probability functions. We extend this result to the case when the martingale…

Probability · Mathematics 2007-05-23 Abhay G Bhatt , Rajeeva L Karandikar , B V Rao

Spatial birth-and-death processes with a finite number of particles are obtained as unique solutions to certain stochastic equations. Conditions are given for existence and uniqueness of such solutions, as well as for continuous dependence…

Probability · Mathematics 2015-02-25 Viktor Bezborodov

Spatial birth-and-death processes with time dependent rates are obtained as solutions to certain stochastic equations. The existence, uniqueness, uniqueness in law and the strong Markov property of unique solutions are proven when the…

Probability · Mathematics 2022-04-22 Viktor Bezborodov , Luca Di Persio

Interacting particle systems are continuous time Markov processes which are used to construct models in many disciplines. Monotonicity is a property that some interacting particle systems possess. A monotone interacting particle system is…

Probability · Mathematics 2010-11-11 Joseph Stover

Consider a continuous time particle system $\eta^t=(\eta^t(k),k\in \mathbb{L})$, indexed by a lattice $\mathbb{L}$ which will be either $\mathbb{Z}$, $\mathbb{Z}/n\mathbb{Z}$, a segment $\{1,\cdots, n\}$, or $\mathbb{Z}^d$, and taking its…

Probability · Mathematics 2019-01-11 Luis Fredes , Jean-François Marckert

We construct a four-parameter family of Markov processes on infinite Gelfand-Tsetlin schemes that preserve the class of central (Gibbs) measures. Any process in the family induces a Feller Markov process on the infinite-dimensional boundary…

Probability · Mathematics 2013-03-04 Alexei Borodin , Grigori Olshanski

The dynamics of the solutions to a class of conservative SPDEs are analysed from two perspectives: Firstly, a probabilistic construction of a corresponding random dynamical system is given for the first time. Secondly, the existence and…

Probability · Mathematics 2022-06-30 Benjamin Fehrman , Benjamin Gess , Rishabh S. Gvalani

We estimate the distance in total variation between the law of a finite state Markov process at time t, starting from a given initial measure, and its unique invariant measure. We derive upper bounds for the time to reach the equilibrium.…

Probability · Mathematics 2015-06-26 Pierre MATHIEU , Pierre PICCO

We present sufficient conditions, in terms of the jumping kernels, for two large classes of conservative Markov processes of pure-jump type to be purely discontinuous martingales with finite second moment. As an application, we establish…

Probability · Mathematics 2020-09-01 Yuichi Shiozawa , Jian Wang

Random dynamical systems with countably many maps which admit countable Markov partitions on complete metric spaces such that the resulting Markov systems are uniformly continuous and contractive are considered. A non-degeneracy and a…

Dynamical Systems · Mathematics 2014-11-18 Ivan Werner

We study infinite systems of particles which undergo coalescence and fragmentation, in a manner determined solely by their masses. A pair of particles having masses $x$ and $y$ coalesces at a given rate $K(x,y)$. A particle of mass $x$…

Probability · Mathematics 2015-08-07 Eduardo Cepeda

This paper introduces the Attracting Random Walks model, which describes the dynamics of a system of particles on a graph with $n$ vertices. At each step, a single particle moves to an adjacent vertex (or stays at the current one) with…

Probability · Mathematics 2020-06-01 Julia Gaudio , Yury Polyanskiy

When two Markov operators commute, it suggests that we can couple two copies of one of the corresponding processes. We explicitly construct a number of couplings of this type for a commuting family of Markov processes on the set of…

Probability · Mathematics 2008-11-20 Anthony P. Metcalfe , Neil O'Connell , Jon Warren
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