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We are concerned with the asymptotics of the Markov chain given by the post-jump locations of a certain piecewise-deterministic Markov process with a state-dependent jump intensity. We provide sufficient conditions for such a model to…

Probability · Mathematics 2024-03-26 Dawid Czapla , Joanna Kubieniec

We explore the concept of a consistent exchangeable survival process - a joint distribution of survival times in which the risk set evolves as a continuous-time Markov process with homogeneous transition rates. We show a correspondence with…

Statistics Theory · Mathematics 2015-08-10 Walter Dempsey , Peter McCullagh

Velocity increments over a distance r and turbulent energy dissipation on a box of size r are well described by the multifractal models of fully developed turbulence. These quantities and models however, do not involve time-correlations and…

chao-dyn · Physics 2008-02-03 R. Lima , R. Vilela Mendes

We investigate a piecewise-deterministic Markov process, evolving on a Polish metric space, whose deterministic behaviour between random jumps is governed by some semi-flow, and any state right after the jump is attained by a randomly…

Probability · Mathematics 2020-12-04 Dawid Czapla , Sander C. Hille , Katarzyna Horbacz , Hanna Wojewódka-Ściążko

The continuous generalized exchange-driven growth model (CGEDG) is a system of integro-differential equations describing the evolution of cluster mass under mass exchange. The rate of exchange depends on the masses of the clusters involved…

Probability · Mathematics 2025-06-03 Chun Yin Lam , André Schlichting

The large deviations principle for the empirical measure for both continuous and discrete time Markov processes is well known. Various expressions are available for the rate function, but these expressions are usually as the solution to a…

Probability · Mathematics 2015-06-22 Paul Dupuis , Yufei Liu

A discrete time branching process where the offspring distribution is generation-dependent, and the number of reproductive individuals is controlled by a random mechanism is considered. This model is a Markov chain but, in general, the…

Probability · Mathematics 2024-01-30 Miguel González , Carmen Minuesa , Manuel Mota , Inés del Puerto , Alfonso Ramos

We study the fluctuations of systems modeled by Markov jump processes with periodic generators. We focus on observables defined through time-periodic functions of the system's states or transitions. Using large deviation theory, canonical…

Statistical Mechanics · Physics 2020-04-22 Lydia Chabane , Raphaël Chétrite , Gatien Verley

The large deviations at various levels that are explicit for Markov jump processes satisfying detailed-balance are revisited in terms of the supersymmetric quantum Hamiltonian $H$ that can be obtained from the Markov generator via a…

Statistical Mechanics · Physics 2024-07-15 Cecile Monthus

We consider a Markov jump process on a general state space to which we apply a time-dependent weak perturbation over a finite time interval. By martingale-based stochastic calculus, under a suitable exponential moment bound for the…

Probability · Mathematics 2024-05-14 Alessandra Faggionato , Vittoria Silvestri

We give computable bounds on the rate of convergence of the transition probabilities to the stationary distribution for a certain class of geometrically ergodic Markov chains. Our results are different from earlier estimates of Meyn and…

Probability · Mathematics 2007-05-23 Peter H. Baxendale

In this paper we prove a version of the Berman-Konsowa principle for reversible Markov jump processes on Polish spaces. The Berman-Konsowa principle provides a variational formula for the capacity of a pair of disjoint measurable sets.…

Probability · Mathematics 2016-11-08 F. den Hollander , S. Jansen

The consistency across scales of a recently developed mathematical thermodynamic structure, between a continuous stochastic nonlinear dynamical system (diffusion process with Langevin or Fokker-Planck equations) and its emergent discrete,…

Statistical Mechanics · Physics 2015-10-28 Moises Santillan , Hong Qian

Large deviation results are given for a class of perturbed nonhomogeneous Markov chains on finite state space which formally includes some stochastic optimization algorithms. Specifically, let {P_n} be a sequence of transition matrices on a…

Probability · Mathematics 2007-05-23 Zach Dietz , Sunder Sethuraman

A general method to construct recombinant tree approximations for stochastic volatility models is developed and applied to the Heston model for stock price dynamics. In this application, the resulting approximation is a four tuple Markov…

Computational Finance · Quantitative Finance 2016-08-14 Erdinç Akyıldırım , Yan Dolinsky , H. Mete Soner

IIn this paper we provide predictable and chaotic representations for It\^{o}-Markov additive processes $X$. Such a process is governed by a finite-state CTMC $J$ which allows one to modify the parameters of the It\^{o}-jump process (in…

Probability · Mathematics 2017-08-28 Zbigniew Palmowski , Łukasz Stettner , Anna Sulima

We study a stochastic particle system which is motivated from grain boundary coarsening in two-dimensional networks. Each particles lives on the positive real line and is labeled as belonging to either Species 1 or Species 2. Species 1…

Probability · Mathematics 2021-07-06 Joseph Klobusicky

The paper deals with the asymptotic properties of a random jump process in a high contrast periodic medium in $\mathbb R^d$, $d\geq 1$. We show that if the coordinates of the random jump process in $\mathbb R^d$ are equipped with an extra…

Probability · Mathematics 2024-02-13 Andrey Piatnitski , Elena Zhizhina

In this paper, we proved moderate deviation principles for a fully coupled two-time-scale stochastic systems, where the slow process is given by stochastic differential equations with small noise, while the fast process is a rapidly…

Probability · Mathematics 2025-12-02 Hongjiang Qian

We introduce a novel class of generative models based on piecewise deterministic Markov processes (PDMPs), a family of non-diffusive stochastic processes consisting of deterministic motion and random jumps at random times. Similarly to…

Machine Learning · Statistics 2024-11-06 Andrea Bertazzi , Dario Shariatian , Umut Simsekli , Eric Moulines , Alain Durmus