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We consider a class of piecewise-deterministic Markov processes where the state evolves according to a linear dynamical system. This continuous time evolution is interspersed by discrete events that occur at random times and change (reset)…

Systems and Control · Computer Science 2017-11-15 Mohammad Soltani , Abhyudai Singh

This paper provides a probabilistic approach to solve linear equations involving Caputo and Riemann-Liouville type derivatives. Using the probabilistic interpretation of these operators as the generators of interrupted Feller processes, we…

Probability · Mathematics 2015-12-07 M. E. Hernández-Hernández , V. N. Kolokoltsov

The dynamics of a non-Markovian open quantum system described by a general time-local master equation is studied. The propagation of the density operator is constructed in terms of two processes: (i) deterministic evolution and (ii)…

Quantum Physics · Physics 2010-01-21 Kari Harkonen

The distribution of the "mixing time" or the "time to stationarity" in a discrete time irreducible Markov chain, starting in state i, can be defined as the number of trials to reach a state sampled from the stationary distribution of the…

Probability · Mathematics 2014-03-05 Jeffrey J. Hunter

A key task in Bayesian statistics is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). However, without any assumptions, sampling (even approximately) can be #P-hard, and few…

Machine Learning · Computer Science 2018-12-03 Rong Ge , Holden Lee , Andrej Risteski

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

In this paper, we study a non-Markovian generalized relativistic Langevin equation (GRLE). We show that when the memory kernel is a sum of exponentials, the GRLE is equivalent to a Markovian system with added variables. We establish the…

Probability · Mathematics 2026-03-17 Ethan Baker , Manh Hong Duong , Hung Dang Nguyen

A study of time homogeneous, real valued Markov processes with a special property and a non-atomic initial distribution is provided. The new notion of a function of evolution of distribution which determines the dependency between one…

Probability · Mathematics 2022-07-04 Tomasz Bielecki , Jacek Jakubowski , Maciej Wiśniewolski

Continuous time random walks have been developed as a straightforward generalisation of classical random walk processes. Some 10 years ago, Fogedby introduced a continuous representation of these processes by means of a set of Langevin…

Data Analysis, Statistics and Probability · Physics 2009-11-13 D. Kleinhans , R. Friedrich

We start by providing an explicit characterization and analytical properties, including the persistence phenomena, of the distribution of the extinction time $\mathbb{T}$ of a class of non-Markovian self-similar stochastic processes with…

Probability · Mathematics 2022-05-24 Ronnie Loeffen , Pierre Patie , Mladen Savov

The contribution of this work is the introduction of a multivariate circular-linear (or poly- cylindrical) distribution obtained by combining the projected and the skew-normal. We show the flexibility of our proposal, its property of…

Methodology · Statistics 2017-11-29 Gianluca Mastrantonio

We discuss a failure of the wide-spread method of images solution to describe the time evolution of probability distribution in diffusive processes with memory. For a path that touches a target during stochastic evolution, we define its…

Statistical Mechanics · Physics 2025-12-02 Takuya Saito , Yuta Sakamoto , Takahiro Sakaue

Semi-Markov processes are a generalization of Markov processes since the exponential distribution of time intervals is replaced with an arbitrary distribution. This paper provides an integro-differential form of the Kolmogorov's backward…

Probability · Mathematics 2017-09-20 Enzo Orsingher , Costantino Ricciuti , Bruno Toaldo

Fractional Brownian motion can be represented as an integral of a deterministic kernel w.r.t. an ordinary Brownian motion either on infinite or compact interval. In previous literature fractional L\'evy processes are defined by integrating…

Probability · Mathematics 2011-11-11 Heikki Tikanmäki , Yuliya Mishura

We demonstrate the equivalence of a Non--Markovian evolution equation with a linear memory--coupling and a Fokker--Planck equation (FPE). In case the feedback term offers a direct and permanent coupling of the current probability density to…

Statistical Mechanics · Physics 2009-11-11 Knud Zabrocki , Steffen Trimper , Svetlana Tatur , Reinhard Mahnke

We exhibit conditions under which the flow of marginal distributions of a discontinuous semimartingale $\xi$ can be matched by a Markov process, whose infinitesimal generator is expressed in terms of the local characteristics of $\xi$. Our…

Probability · Mathematics 2012-05-17 Amel Bentata , Rama Cont

This paper is a step in the direction of understanding the behavior of non-intersecting Brownian motions on the real line, when the number of particles becomes large. Consider 2k non-intersecting Brownian motions, all starting at the…

Probability · Mathematics 2007-05-23 Mark Adler , Pierre van Moerbeke

In this paper, we establish a probabilistic representation as well as some integration by parts formulae for the marginal law at a given time maturity of some stochastic volatility model with unbounded drift. Relying on a perturbation…

Probability · Mathematics 2020-11-23 Junchao Chen , Noufel Frikha , Houzhi Li

The study of distributed order calculus usually concerns about fractional derivatives of the form $\int_0^1 \partial^\alpha u \, m(d\alpha)$ for some measure $m$, eventually a probability measure. In this paper an approach based on L\'evy…

Probability · Mathematics 2015-05-20 Bruno Toaldo

We extend the Langevin Monte Carlo (LMC) algorithm to compactly supported measures via a projection step, akin to projected Stochastic Gradient Descent (SGD). We show that (projected) LMC allows to sample in polynomial time from a…

Probability · Mathematics 2016-08-08 Sébastien Bubeck , Ronen Eldan , Joseph Lehec