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We review and extend the formalism introduced by Peliti, that maps a Markov process to a path-integral representation. After developing the mapping, we apply it to some illustrative examples: the simple decay process, the birth-and-death…

Statistical Mechanics · Physics 2015-06-24 Ronald Dickman , Ronaldo Vidigal

We study the long-time behavior of stochastic models with an absorbing state, conditioned on survival. For a large class of processes, in which saturation prevents unlimited growth, statistical properties of the surviving sample attain…

Statistical Mechanics · Physics 2009-11-07 Ronald Dickman , Ronaldo Vidigal

The approximation of integral functionals with respect to a stationary Markov process by a Riemann-sum estimator is studied. Stationarity and the functional calculus of the infinitesimal generator of the process are used to get a better…

Probability · Mathematics 2016-10-18 Randolf Altmeyer , Jakub Chorowski

We study in this paper the large-time asymptotics of the empirical vector associated with a family of finite-state mean-field systems with multi-classes. The empirical vector is composed of local empirical measures characterizing the…

Probability · Mathematics 2021-08-18 Donald A. Dawson , Ahmed Sid-Ali , Yiqiang Q. Zhao

The aim of this paper is to study the asymptotic behavior of a system of birth and death processes in mean field type interaction in discrete space. We first establish the exponential convergence of the particle system to equilibrium for a…

Probability · Mathematics 2015-10-13 Marie-Noémie Thai

We prove under mild conditions that the Fleming-Viot process selects the minimal quasi-stationary distribution for Markov processes with soft killing on non-compact state spaces. Our results are applied to multi-dimensional birth and death…

Probability · Mathematics 2018-10-17 Nicolas Champagnat , Denis Villemonais

We study the problem of parameter estimation for large exchangeable interacting particle systems when a sample of discrete observations from a single particle is known. We propose a novel method based on martingale estimating functions…

Numerical Analysis · Mathematics 2024-01-30 Grigorios A. Pavliotis , Andrea Zanoni

Momentum-space representation renders an interesting perspective to theory of large fluctuations in populations undergoing Markovian stochastic gain-loss processes. This representation is obtained when the master equation for the…

Statistical Mechanics · Physics 2015-05-18 Michael Assaf , Baruch Meerson , Pavel V. Sasorov

We obtain necessary and sufficient conditions for the regular variation of the variance of partial sums of functionals of discrete and continuous-time stationary Markov processes with normal transition operators. We also construct a class…

Probability · Mathematics 2014-05-13 George Deligiannidis , Magda Peligrad , Sergey Utev

The statement of the mean field approximation theorem in the mean field theory of Markov processes particularly targets the behaviour of population processes with an unbounded number of agents. However, in most real-world engineering…

Probability · Mathematics 2017-05-11 Mahmoud Talebi , Jan Friso Groote , Jean-Paul Linnartz

In this paper, we develop necessary and sufficient conditions for the validity of a martingale approximation for the partial sums of a stationary process in terms of the maximum of consecutive errors. Such an approximation is useful for…

Probability · Mathematics 2011-02-11 Mikhail Gordin , Magda Peligrad

We introduce a system of self-propelled agents (active Brownian particles) with velocity alignment in two spatial dimensions and derive a mean-field theory from the microscopic dynamics via a nonlinear Fokker-Planck equation and a moment…

Statistical Mechanics · Physics 2011-09-26 Pawel Romanczuk , Lutz Schimansky-Geier

We introduce a variational theory for processes adapted to the multi-dimensional Brownian motion filtration. The theory provides a differential structure which describes the infinitesimal evolution of Wiener functionals at very small…

Probability · Mathematics 2017-07-13 Alberto Ohashi , Dorival Leão , Alexandre B. Simas

We introduce state-space models where the functionals of the observational and the evolutionary equations are unknown, and treated as random functions evolving with time. Thus, our model is nonparametric and generalizes the traditional…

Methodology · Statistics 2014-02-24 Anurag Ghosh , Soumalya Mukhopadhyay , Sandipan Roy , Sourabh Bhattacharya

We present normal approximation results at the process level for local functionals defined on dynamic Poisson processes in $\mathbb{R}^d$. The dynamics we study here are those of a Markov birth-death process. We prove functional limit…

Probability · Mathematics 2022-10-25 Efe Onaran , Omer Bobrowski , Robert J. Adler

Markov decision process over vector addition system with states (VASS MDP) is a finite state model combining non-deterministic and probabilistic behavior, augmented with non-negative integer counters that can be incremented or decremented…

Formal Languages and Automata Theory · Computer Science 2025-03-10 Michal Ajdarów

We study the non-parametric estimation of an unknown stationary density fV of an unobserved strictly stationary volatility process $(\bm V_t)_{t\geq 0}$ on $\IRp^2 := (0,\infty)^2$ based on discrete-time observations in a stochastic…

Statistics Theory · Mathematics 2022-10-04 Sergio Brenner Miguel

We continue the investigation of the spectral theory and exponential asymptotics of Markov processes, following Kontoyiannis and Meyn (2003). We introduce a new family of nonlinear Lyapunov drift criteria, characterizing distinct subclasses…

Probability · Mathematics 2007-05-23 Ioannis Kontoyiannis , S. P. Meyn

When clustering molecular dynamics (MD) trajectories into a few metastable conformational states, the Markov state models (MSMs) assumption of timescale separation between fast intrastate fluctuations and rarely occurring interstate…

Soft Condensed Matter · Physics 2025-01-17 Sofia Sartore , Franziska Teichmann , Gerhard Stock

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
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