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We construct optimal Markov couplings of L\'{e}vy processes, whose L\'evy (jump) measure has an absolutely continuous component. The construction is based on properties of subordinate Brownian motions and the coupling of Brownian motions by…

Probability · Mathematics 2011-05-17 Björn Böttcher , René L. Schilling , Jian Wang

We study the optimal Markovian coupling problem for two Pi-valued Feller processes {X_t} and {Y_t}, which seeks a coupling process {(X_t, Y_t)} that minimizes the right derivative at t = 0 of the expected cost E^{(x,y)}[c(X_t, Y_t)], for…

Probability · Mathematics 2025-09-30 Wei Yang Kang , Tau Shean Lim

Markovian maximal couplings of Markov processes are characterized by an equality of total variation and a distance of Wasserstein type. If a Markovian maximal coupling is a Feller process, the generator can be calculated, e.g. for…

Probability · Mathematics 2017-10-27 Björn Böttcher

This paper presents a self-contained account for coupling arguments and applications in the context of Markov processes. We first use coupling to describe the transport problem, which leads to the concepts of optimal coupling and…

Probability · Mathematics 2010-12-30 Feng-Yu Wang

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

In this article, we define the new concept of local coupling property for Markov processes and study its relationship with distributional properties of the transition probability. In the special case of L\'evy processes we show that this…

Probability · Mathematics 2018-02-28 Kasra Alishahi , Erfan Salavati

The paper is devoted to the relationship between the continuous Markovian description of Levy flights developed previously and their equivalent representation in terms of discrete steps of a wandering particle, a certain generalization of…

Statistical Mechanics · Physics 2015-06-04 Ihor Lubashevsky

The notion of a successful coupling of Markov processes, based on the idea that both components of the coupled system ``intersect'' in finite time with probability one, is extended to cover situations when the coupling is unnecessarily…

Probability · Mathematics 2007-05-23 Michael Blank , Sergey Pirogov

In this paper we develop a general framework for constructing and analysing coupled Markov chain Monte Carlo samplers, allowing for both (possibly degenerate) diffusion and piecewise deterministic Markov processes. For many performance…

Probability · Mathematics 2018-06-29 N. Nuesken , G. A. Pavliotis

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

This article treats both discrete time and continuous time stopping problems for general Markov processes on the real line with general linear costs. Using an auxiliary function of maximum representation type, conditions are given to…

Probability · Mathematics 2020-01-28 Sören Christensen , Tobias Sohr

In the literature on optimal stopping, the problem of maximizing the expected discounted reward over all stopping times has been explicitly solved for some special reward functions (including $(x^+)^{\nu}$, $(e^x-K)^+$, $(K-e^{-x})^+$,…

Probability · Mathematics 2017-10-13 Yi-Shen Lin , Yi-Ching Yao

We study a Monte Carlo algorithm for simulation of probability distributions based on stochastic step functions, and compare to the traditional Metropolis/Hastings method. Unlike the latter, the step function algorithm can produce an…

Probability · Mathematics 2015-12-07 Torquil Macdonald Sørensen , Fred Espen Benth

Maximal couplings are (probabilistic) couplings of Markov processes such that the tail probabilities of the coupling time attain the total variation lower bound (Aldous bound) uniformly for all time. Markovian (or immersion) couplings are…

Probability · Mathematics 2016-03-29 Sayan Banerjee , Wilfrid S. Kendall

In this paper, we present two completely uncoupled algorithms for utility maximization. In the first part, we present an algorithm that can be applied for general non-concave utilities. We show that this algorithm induces a perturbed (by…

Systems and Control · Computer Science 2018-09-18 S. Ramakrishnan , Venkatesh Ramaiyan

By the probabilistic coupling approach which combines a new refined basic coupling with the synchronous coupling for L\'evy processes, we obtain explicit exponential contraction rates in terms of the standard $L^1$-Wasserstein distance for…

Probability · Mathematics 2024-02-20 Yao Liu , Jian Wang , Meng-ge Zhang

Consider a Brownian motion on the circumference of the unit circle, which jumps to the opposite point of the circumference at incident times of an independent Poisson process of rate $\lambda$. We examine the problem of coupling two copies…

Probability · Mathematics 2023-05-10 Stephen B. Connor , Roberta Merli

There has been a recent surge of interest in coupling methods for Markov chain Monte Carlo algorithms: they facilitate convergence quantification and unbiased estimation, while exploiting embarrassingly parallel computing capabilities.…

Computation · Statistics 2025-09-03 Tamás P. Papp , Chris Sherlock

We give necessary and sufficient conditions guaranteeing that the coupling for L\'evy processes (with non-degenerate jump part) is successful. Our method relies on explicit formulae for the transition semigroup of a compound Poisson process…

Probability · Mathematics 2015-05-19 René L. Schilling , Jian Wang

We propose a new framework for formulating optimal transport distances between Markov chains. Previously known formulations studied couplings between the entire joint distribution induced by the chains, and derived solutions via a reduction…

Machine Learning · Computer Science 2024-06-17 Sergio Calo , Anders Jonsson , Gergely Neu , Ludovic Schwartz , Javier Segovia-Aguas
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