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In this paper, we extend the results of Elliott and Yang \cite{elliott3} and discuss the control of a stochastic process for which the driving noise is provided by a martingale associated with a semi-Markov Chain. An existence and a…

Probability · Mathematics 2025-12-23 Robert J. Elliott , Zhe Yang

The dynamics of a Markov process are often specified by its infinitesimal generator or, equivalently, its symbol. This paper contains examples of analytic symbols which do not determine the law of the corresponding Markov process uniquely.…

Probability · Mathematics 2020-08-14 Jan Kallsen , Paul Krühner

Markovian diffusion processes yield a system of conservation laws which couple various conditional expectation values (local moments). Solutions of that closed system of deterministic partial differential equations stand for a regular…

Statistical Mechanics · Physics 2007-05-23 P. Garbaczewski

We analyze the long-time behavior of numerical schemes for a class of monotone stochastic partial differential equations (SPDEs) driven by multiplicative noise. By deriving several time-independent a priori estimates for the numerical…

Numerical Analysis · Mathematics 2025-01-27 Zhihui Liu

In this paper, the three-dimensional stochastic nonhomogeneous incompressible Navier-Stokes equations driven by L\'evy process consisting of the Brownian motion, the compensated Poisson random measure and the Poisson random measure are…

Analysis of PDEs · Mathematics 2018-01-23 Robin Ming Chen , Dehua Wang , Huaqiao Wang

Consider the continuous-time Markov Branching Process. In critical case we consider a situation when the generating function of intensity of transformation of particles has the infinite second moment, but its tail regularly varies in sense…

Probability · Mathematics 2022-01-07 Azam Imomov

We consider a class of semi-Markov processes (SMP) such that the embedded discrete time Markov chain may be non-homogeneous. The corresponding augmented processes are represented as semi-martingales using stochastic integral equation…

Probability · Mathematics 2022-07-14 Anindya Goswami , Subhamay Saha , Ravishankar Kapildev Yadav

The solutions of SDEs with multiplicative noise are not Markovian. On a coarse-grained time scale they still are, but only in the "anti-Ito" case. This allows a simple computation of the most likely path. Any density peak moves along such a…

General Physics · Physics 2021-09-27 Dietrich Ryter

We consider a randomly forced Ginzburg-Landau equation on an unbounded domain. The forcing is smooth and homogeneous in space and white noise in time. We prove existence and smoothness of solutions, existence of an invariant measure for the…

Analysis of PDEs · Mathematics 2007-05-23 Jacques Rougemont

We consider the problem of conditioning a Markov process on a rare event and of representing this conditioned process by a conditioning-free process, called the effective or driven process. The basic assumption is that the rare event used…

Statistical Mechanics · Physics 2015-08-17 Raphael Chetrite , Hugo Touchette

We consider the model of Brownian motion indexed by the Brownian tree, which has appeared in a variety of different contexts in probability, statistical physics and combinatorics. For this model, the total occupation measure is known to…

Probability · Mathematics 2023-06-16 Jean-François Le Gall

We present for the first time an asymptotic convergence analysis of two time-scale stochastic approximation driven by "controlled" Markov noise. In particular, the faster and slower recursions have non-additive controlled Markov noise…

Machine Learning · Computer Science 2020-12-03 Prasenjit Karmakar

Examples of stochastic processes whose state space representations involve functions of an integral type structure $$I_{t}^{(a,b)}:=\int_{0}^{t}b(Y_{s})e^{-\int_{s}^{t}a(Y_{r})dr}ds, \quad t\ge 0$$ are studied under an ergodic…

Probability · Mathematics 2025-02-25 Abhishek Pal Majumder

The representation theorem is obtained for functionals of non-Markov processes and their first exit times from bounded domains. These functionals are represented via solutions of backward parabolic Ito equations. As an example of…

Probability · Mathematics 2010-07-29 Nikolai Dokuchaev

By using limit theorems of uniform mixing Markov processes and martingale difference sequences, the strong law of large numbers, central limit theorem, and the law of iterated logarithm are established for additive functionals of…

Probability · Mathematics 2019-04-08 Jianhai Bao , Feng-Yu Wang , Chenggui Yuan

A Fokker-Planck equation approach for the treatment of non-Markovian stochastic processes is proposed. The approach is based on the introduction of fictitious trajectories sharing with the real ones their local structure and initial…

Chaotic Dynamics · Physics 2009-11-11 Piero Olla , Luca Pignagnoli

We consider the decreasing and the increasing $r$-excessive functions $\varphi_r$ and $\psi_r$ that are associated with a one-dimensional conservative regular continuous strong Markov process $X$ with values in an interval with endpoints…

Probability · Mathematics 2016-12-28 Mikhail Urusov , Mihail Zervos

In the recent literature dealing with spatial extensions of the continuous Ramsey model, the capital accumulation process via time and space is modeled as a linear parabolic partial differential equation. The process of capital movement…

Optimization and Control · Mathematics 2019-09-06 L. Fredrick , G. Müller-Fürstenberger , E. W. Sachs , L. Somorowsky

We prove existence and uniqueness of strong solutions for a class of semilinear stochastic evolution equations driven by general Hilbert space-valued semimartingales, with drift equal to the sum of a linear maximal monotone operator in…

Probability · Mathematics 2019-11-01 Carlo Marinelli , Luca Scarpa

Supermartingales are here defined on a non-probabilistic setting and can be interpreted solely in terms of superhedging operations. The classical expectation operator is replaced by a pair of subadditive operators one of them providing a…

Probability · Mathematics 2023-12-26 C. Bender , S. E. Ferrando , K. Gajewski , A. L. Gonzalez