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We consider a heat kernel approach for the development of stochastic pricing kernels. The kernels are constructed by positive propagators, which are driven by time-inhomogeneous Markov processes. We multiply such a propagator with a…

Computational Finance · Quantitative Finance 2010-12-10 Jiro Akahori , Andrea Macrina

We study inhomogeneous continuous-time weakly ergodic Markov chains with a finite state space. We introduce the notion of a Markov chain with the regular structure of an infinitesimal matrix and study the sharp upper bounds on the rate of…

Probability · Mathematics 2020-02-17 A. I. Zeifman , Y. A. Satin , K. M. Kiseleva

In this work, we consider an inhomogeneous (discrete time) Markov chain and are interested in its long time behavior. We provide sufficient conditions to ensure that some of its asymptotic properties can be related to the ones of a…

Probability · Mathematics 2017-11-09 Michel Benaïm , Florian Bouguet , Bertrand Cloez

Time homogeneous polynomial processes are Markov processes whose moments can be calculated easily through matrix exponentials. In this work, we develop a notion of time inhomogeneous polynomial processes where the coeffiecients of the…

Probability · Mathematics 2018-06-12 María Fernanda del Carmen Agoitia Hurtado , Thorsten Schmidt

We study perturbation theory and uniform ergodicity for discrete-time Markov chains on general state spaces in terms of the uniform moments of the first hitting times on some set. The methods we adopt are different from previous ones. For…

Probability · Mathematics 2020-03-17 Yonghua Mao , Yanhong Song

Motivated by a model presented by S. Gudder, we study a quantum generalization of Markov chains and discuss the relation between these maps and open quantum random walks, a class of quantum channels described by S. Attal et al. We consider…

Quantum Physics · Physics 2016-08-10 Carlos F. Lardizabal , Rafael R. Souza

We develop a new framework for branched transport between probability measures which are allowed to vary in time. This framework can be used to model problems where the underlying transportation network displays a branched structure, but…

Optimization and Control · Mathematics 2025-12-25 Jun Kitagawa , Cecilia Mikat

All one-condition generalized inverses of the Markovian kernel I - P, where P is the transition matrix of a finite irreducible Markov chain, can be uniquely specified in terms of the stationary probabilities and the mean first passage times…

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

In this paper, we present a kernel-based, multi-task Gaussian Process (GP) model for approximating the underlying function of an individual's mobility state using a time-inhomogeneous Markov Process with two states: moves and pauses. Our…

Machine Learning · Statistics 2023-06-22 Ekin Ugurel

We study discrete time Markov processes with periodic or open boundary conditions and with inhomogeneous rates in the bulk. The Markov matrices are given by the inhomogeneous transfer matrices introduced previously to prove the…

Statistical Mechanics · Physics 2015-10-30 N. Crampe , K. Mallick , E. Ragoucy , M. Vanicat

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

We study Markov processes where the "time" parameter is replaced by paths in a directed graph from an initial vertex to a terminal one. Along each directed path the process is Markov and has the same distribution as the one along any other…

Probability · Mathematics 2012-11-16 Krzysztof Burdzy , Soumik Pal

Imprecise continuous-time Markov chains are a robust type of continuous-time Markov chains that allow for partially specified time-dependent parameters. Computing inferences for them requires the solution of a non-linear differential…

Probability · Mathematics 2018-10-11 Alexander Erreygers , Jasper De Bock

Given an ergodic finite-state Markov chain, let M_{iw} denote the mean time from i to equilibrium, meaning the expected time, starting from i, to arrive at a state selected randomly according to the equilibrium measure w of the chain. John…

Probability · Mathematics 2009-09-16 Peter G. Doyle

We study the problem of characterizing the expected hitting times for a robust generalization of continuous-time Markov chains. This generalization is based on the theory of imprecise probabilities, and the models with which we work…

Probability · Mathematics 2022-06-28 Thomas Krak

We present a novel approach to quantizing Markov chains. The approach is based on the Markov chain coupling method, which is frequently used to prove fast mixing. Given a particular coupling, e.g., a grand coupling, we construct a…

Quantum Physics · Physics 2025-12-24 Kristan Temme , Pawel Wocjan

We study some regularity properties in locally stationary Markov models which are fundamental for controlling the bias of nonparametric kernel estimators. In particular, we provide an alternative to the standard notion of derivative process…

Statistics Theory · Mathematics 2018-12-07 Lionel Truquet

In this paper, we consider two time-inhomogeneous Markov chains $X^{(l)}_t$, $l\in\{1,2\}$, with discrete time on a general state space. We assume the existence of some renewal set $C$ and investigate the time of simultaneous renewal, that…

Probability · Mathematics 2017-01-06 Vitaliy Golomoziy

We consider an additive functional driven by a time-inhomogeneous Markov chain with a finite state space. Our study focuses on the joint distribution of the two-sided exit time and the state of the driving Markov chain at the time of exit,…

Probability · Mathematics 2023-07-06 Tomasz R. Bielecki , Ziteng Cheng , Ruoting Gong

Permanental sequences with non-symmetric kernels that are generalization of the potentials of a Markov chain with state space $\{0,1/2, \ldots, 1/n,\ldots\}$ that was introduced by Kolmogorov, are studied. Depending on a parameter in the…

Probability · Mathematics 2020-05-12 Michael B. Marcus , Jay Rosen