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We consider a continuous time Markov chain on a countable state space and prove a joint large deviation principle for the empirical measure and the empirical flow, which accounts for the total number of jumps between pairs of states. We…

Probability · Mathematics 2015-01-19 Lorenzo Bertini , Alessandra Faggionato , Davide Gabrielli

By refining a recent result of Xie and Zhang, we prove the exponential ergodicity under a weighted variation norm for singular SDEs with drift containing a local integrable term and a coercive term. This result is then extended to singular…

Probability · Mathematics 2023-03-10 Feng-Yu Wang

We consider a stochastic process which is (a) described by a continuous-time Markov chain on only short time-scales and (b) constrained to conserve a number of hidden quantities on long time-scales. We assume that the transition matrix of…

Statistical Mechanics · Physics 2020-10-27 Vitaly Vanchurin

We study the symmetry of large deviation functions associated with time-integrated currents in Markov pure jump processes. One current known to have this symmetry is the fluctuating entropy production and this is the content of the…

Statistical Mechanics · Physics 2012-12-03 A. C. Barato , R. Chetrite

We consider a class of continuous time Markov chains on a compact metric space that admit an invariant measure strictly positive on open sets together with absorbing states. We prove the joint large deviation principle for the empirical…

Probability · Mathematics 2015-12-04 Giada Basile , Lorenzo Bertini

For discrete-time Markov chains on general state spaces, we establish criteria for non-ergodicity and non-strong ergodicity, and derive sufficient conditions for non-geometric ergodicity via the theory of minimal nonnegative solutions. Our…

Probability · Mathematics 2025-12-29 Ling-Di Wang , Yu Chen , Yu-Hui Zhang

Markov branching systems form a fundamental class of stochastic models that are extensively applied in biology, physics, finance, and other domains. These systems are distinguished by their continuous-time evolution and inherent branching…

We consider a class of generalized long-range exclusion processes evolving either on $\mathbb Z$ or on a finite lattice with an open boundary. The jump rates are given in terms of a general kernel depending on both the departure and…

Probability · Mathematics 2024-10-24 Patrícia Gonçalves , Julian Kern , Lu Xu

We consider Markov processes in continuous time with state space $\posint^N$ and provide two sufficient conditions and one necessary condition for the existence of moments $E(\|X(t)\|^r)$ of all orders $r \in \nat$ for all $t \geq 0$. The…

Probability · Mathematics 2015-02-02 Muruhan Rathinam

Piecewise-deterministic Markov processes form a general class of non-diffusion stochastic models that involve both deterministic trajectories and random jumps at random times. In this paper, we state a new characterization of the jump rate…

Methodology · Statistics 2017-05-03 Romain Azaïs , Alexandre Genadot

We propose a method to approximate continuous-time, continuous-state stochastic processes by a discrete-time Markov chain defined on a nonuniform grid. Our method provides exact moment matching for processes whose first and second moments…

Probability · Mathematics 2025-11-27 Do Hyun Kim , Ahmet Cetinkaya

In this brief paper we find computable exponential convergence rates for a large class of stochastically ordered Markov processes. We extend the result of Lund, Meyn, and Tweedie (1996), who found exponential convergence rates for…

Probability · Mathematics 2018-10-19 Julia Gaudio , Saurabh Amin , Patrick Jaillet

In this paper, we study the convergence for solutions to a sequence of (possibly degenerate) stochastic differential equations with jumps, when the coefficients converge in some appropriate sense. Our main tools are the superposition…

Probability · Mathematics 2025-06-18 Huijie Qiao

Consider a Markov chain $\{X_n\}_{n\ge 0}$ with an ergodic probability measure $\pi$. Let $\Psi$ a function on the state space of the chain, with $\alpha$-tails with respect to $\pi$, $\alpha\in (0,2)$. We find sufficient conditions on the…

Probability · Mathematics 2009-12-15 Milton Jara , Tomasz Komorowski , Stefano Olla

We consider dynamics of the empirical measure of vertex neighborhood states of Markov interacting jump processes on sparse random graphs, in a suitable asymptotic limit as the graph size goes to infinity. Under the assumption of a certain…

Probability · Mathematics 2025-02-10 Juniper Cocomello , Michel Davydov , Kavita Ramanan

We study a class of Piecewise Deterministic Markov Processes with state space Rd x E where E is a finite set. The continuous component evolves according to a smooth vector field that is switched at the jump times of the discrete coordinate.…

Probability · Mathematics 2014-04-08 Michel Benaïm , Stéphane Le Borgne , Florent Malrieu , Pierre-André Zitt

For general, almost surely absorbed Markov processes, we obtain necessary and sufficient conditions for exponential convergence to a unique quasi-stationary distribution in the total variation norm. These conditions also ensure the…

Probability · Mathematics 2014-12-25 Nicolas Champagnat , Denis Villemonais

In this paper we prove the convergence to the stochastic Burgers equation from one-dimensional interacting particle systems, whose dynamics allow the degeneracy of the jump rates. To this aim, we provide a new proof of the second order…

Probability · Mathematics 2017-08-30 Oriane Blondel , Patricia Gonçalves , Marielle Simon

Irreversible drift-diffusion processes are very common in biochemical reactions. They have a non-equilibrium stationary state (invariant measure) which does not satisfy detailed balance. For the corresponding Fokker-Planck equation on a…

Numerical Analysis · Mathematics 2023-04-12 Yuan Gao , Jian-Guo Liu

We consider a class of discrete time Markov chains with state space [0,1] and the following dynamics. At each time step, first the direction of the next transition is chosen at random with probability depending on the current location. Then…

Probability · Mathematics 2014-12-04 Shaun McKinlay , Konstantin Borovkov