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By employing the full counting statistics formalism, we characterize the first moment of energy that is exchanged during a generally non-Markovian evolution in non-driven continuous variables systems. In particular, we focus on the…

The convergence rate of a Markov chain to its stationary distribution is typically assessed using the concept of total variation mixing time. However, this worst-case measure often yields pessimistic estimates and is challenging to infer…

Statistics Theory · Mathematics 2026-02-06 Geoffrey Wolfer , Pierre Alquier

Couplings play a central role in contemporary Markov chain Monte Carlo methods and in the analysis of their convergence to stationarity. In most cases, a coupling must induce relatively fast meeting between chains to ensure good…

Methodology · Statistics 2021-02-04 John O'Leary

We give potential theoretic estimates for the probability that a set $A$ contains a double point of planar Brownian motion run for unit time. Unlike the probability for $A$ to intersect the range of a Markov process, this cannot be…

Probability · Mathematics 2009-09-29 Robin Pemantle , Yuval Peres

In a wireless network, the efficiency of scheduling algorithms over time-varying channels depends heavily on the accuracy of the Channel State Information (CSI), which is usually quite ``costly'' in terms of consuming network resources.…

Networking and Internet Architecture · Computer Science 2014-04-01 Wenzhuo Ouyang , Atilla Eryilmaz , Ness B. Shroff

Overdamped motion of Brownian particles in tilted piecewise linear periodic potentials is considered. Explicit algebraic expressions for the diffusion coefficient, current, and coherence level of Brownian transport are derived. Their…

Soft Condensed Matter · Physics 2009-11-10 Els Heinsalu , Risto Tammelo , Teet Ord

We consider the motion of a particle governed by a weakly random Hamiltonian flow. We identify temporal and spatial scales on which the particle trajectory converges to a spatial Brownian motion. The main technical issue in the proof is to…

Mathematical Physics · Physics 2009-11-11 T. Komorowski , L. Ryzhik

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

We consider a molecular machine described as a Brownian particle diffusing in a tilted periodic potential. We evaluate the absorbed and released power of the machine as a function of the applied molecular and chemical forces, by using the…

Statistical Mechanics · Physics 2012-03-23 N. Golubeva , A. Imparato , L. Peliti

In this paper we study the bicausal optimal transport problem for Markov chains, an optimal transport formulation suitable for stochastic processes which takes into consideration the accumulation of information as time evolves. Our analysis…

Probability · Mathematics 2020-10-15 Vrettos Moulos

Brownian motion is the only random process which is Gaussian, stationary and Markovian. Dropping the Markovian property, i.e. allowing for memory, one obtains a class of processes called fractional Brownian motion, indexed by the Hurst…

Statistical Mechanics · Physics 2016-07-27 Mathieu Delorme , Kay Jörg Wiese

We study the role of non-Markovian effects in the emission spectrum of a quantum emitter resonantly coupled to a surface plasmon in a metal-dielectric structure as the system transitions to strong coupling regime. By using a recent quantum…

Mesoscale and Nanoscale Physics · Physics 2022-07-08 Tigran V. Shahbazyan

We present an efficient algorithm that, given a discrete random variable $X$ and a number $m$, computes a random variable whose support is of size at most $m$ and whose Kolmogorov distance from $X$ is minimal, also for the one-sided…

Machine Learning · Statistics 2022-07-19 Liat Cohen , Tal Grinshpoun , Gera Weiss

In this article we review existing literature on dynamic copulas and then propose an n-copula which varies in time and space. Our approach makes use of stochastic differential equations, and gives rise to a dynamic copula which is able to…

Statistics Theory · Mathematics 2008-12-18 Glenis Crane

In a series of recent works, Boyd, Diaconis, and their co-authors have introduced a semidefinite programming approach for computing the fastest mixing Markov chain on a graph of allowed transitions, given a target stationary distribution.…

Probability · Mathematics 2011-09-07 S. Roch

The migration of active particles in slowly moving, crowded, and heterogeneous media is fundamental to various biological processes and technological applications, such as cargo transport. In this study, we numerically investigate the…

Soft Condensed Matter · Physics 2024-12-04 Meng-Yuan Li , Ning Zheng , Yan-Wei Li

We provide a detailed discussion about the unitary and reduced evolution induced by family of Hamiltonian models describing a multilevel system, with a ground state and a possibly multilevel excited sector, coupled to a multimode boson…

Quantum Physics · Physics 2023-01-30 Dariusz Chruściński , Samaneh Hesabi , Davide Lonigro

I derive a temporally propagated uni-directional optical pulse equation valid in the few cycle limit. Temporal propagation is advantageous because it naturally preserves causality, unlike the competing spatially propagated models. The exact…

Optics · Physics 2018-06-04 Paul Kinsler

Several Markovian process calculi have been proposed in the literature, which differ from each other for various aspects. With regard to the action representation, we distinguish between integrated-time Markovian process calculi, in which…

Logic in Computer Science · Computer Science 2010-06-09 Marco Bernardo

Motivated by nucleation and molecular aggregation in physical, chemical and biological settings, we present a thorough analysis of the general problem of stochastic self-assembly of a fixed number of identical particles in a finite volume.…

Statistical Mechanics · Physics 2015-06-12 Romain Yvinec , Maria R. D'Orsogna , Tom Chou
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