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Related papers: Markov processes on time-like graphs

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We develop a systematic matrix-analytic approach, based on intertwinings of Markov semigroups, for proving theorems about hitting-time distributions for finite-state Markov chains -- an approach that (sometimes) deepens understanding of the…

Probability · Mathematics 2012-09-04 James Allen Fill , Vince Lyzinski

In this paper, we study a notion of local stationarity for discrete time Markov chains which is useful for applications in statistics. In the spirit of some locally stationary processes introduced in the literature, we consider triangular…

Statistics Theory · Mathematics 2016-10-06 Lionel Truquet

We study a limit behavior of a sequence of Markov processes (or Markov chains) such that their distributions outside of any neighborhood of a "singular" point attract to some probability law. In any neighborhood of this point the behavior…

Probability · Mathematics 2015-09-14 Andrey Pilipenko , Yuriy Prykhodko

In this paper, we introduce different concepts of Granger causality and contemporaneous correlation for multivariate stationary continuous-time processes to model different dependencies between the component processes. Several equivalent…

Statistics Theory · Mathematics 2024-08-13 Vicky Fasen-Hartmann , Lea Schenk

We make use of matrix representations of completely positive maps in order to study open quantum dynamics on graphs, with emphasis on quantum walks and the associated trajectories obtained via a monitoring of the position. We discuss the…

Mathematical Physics · Physics 2019-01-08 Carlos F. Lardizabal

We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series…

Statistics Theory · Mathematics 2011-07-18 Michael Eichler

This paper considers the problem of randomized influence maximization over a Markovian graph process: given a fixed set of nodes whose connectivity graph is evolving as a Markov chain, estimate the probability distribution (over this fixed…

Social and Information Networks · Computer Science 2017-11-10 Buddhika Nettasinghe , Vikram Krishnamurthy

Dynamical processes can be transformed into graphs through a family of mappings called visibility algorithms, enabling the possibility of (i) making empirical data analysis and signal processing and (ii) characterising classes of dynamical…

Chaotic Dynamics · Physics 2015-06-18 Lucas Lacasa

In this paper, we are interested in investigating the perturbation bounds for the stationary distributions for discrete-time or continuous-time Markov chains on a countable state space. For discrete-time Markov chains, two new norm-wise…

Probability · Mathematics 2012-08-27 Yuanyuan Liu

A simple lemma bounds $\mathrm{s.d.}(T)/\mathbb{E} T$ for hitting times $T$ in Markov chains with a certain strong monotonicity property. We show how this lemma may be applied to several increasing set-valued processes. Our main result…

Probability · Mathematics 2016-04-22 David J. Aldous

Given a discrete source distribution $\mu$ and discrete target distribution $\nu$ on a common finite state space $\mathcal{X}$, we are tasked with transporting $\mu$ to $\nu$ using a given discrete-time Markov chain $X$ with the quickest…

Probability · Mathematics 2018-07-23 Michael C. H. Choi

The problem of appropriately matching items subject to compatibility constraints arises in a number of important applications. While most of the literature on matching theory focuses on a static setting with a fixed number of items, several…

Probability · Mathematics 2022-01-04 Céline Comte

Comparison results are given for time-inhomogeneous Markov processes with respect to function classes induced stochastic orderings. The main result states comparison of two processes, provided that the comparability of their infinitesimal…

Probability · Mathematics 2015-05-13 Ludger Rueschendorf , Alexander Schnurr , Viktor Wolf

We study the problem of learning the Markov order in categorical sequences that represent paths in a network, i.e. sequences of variable lengths where transitions between states are constrained to a known graph. Such data pose challenges…

Machine Learning · Computer Science 2020-07-07 Luka V. Petrović , Ingo Scholtes

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

We give a short overview of recent results on a specific class of Markov process: the Piecewise Deterministic Markov Processes (PDMPs). We first recall the definition of these processes and give some general results. On more specific cases…

Statistics Theory · Mathematics 2013-09-25 Romain Azaïs , Jean-Baptiste Bardet , Alexandre Genadot , Nathalie Krell , Pierre-André Zitt

We investigate local-density dependent Markov processes on a class of large graphs sampled from a graphon, where the transition rates of the vertices are influenced by the states of their neighbors. We show that as the average degree…

Probability · Mathematics 2020-11-24 Daniel Keliger , Illes Horvath , Balint Takacs

We consider the problem of estimating the expected time to find a maximum degree node on a graph using a (parameterized) biased random walk. For assortative graphs the positive degree correlation serves as a local gradient for which a bias…

Social and Information Networks · Computer Science 2016-02-26 Jonathan Stokes , Steven Weber

We consider a stochastic directed graph on the integers whereby a directed edge between $i$ and a larger integer $j$ exists with probability $p_{j-i}$ depending solely on the distance between the two integers. Under broad conditions, we…

Probability · Mathematics 2017-11-29 Denis Denisov , Sergey Foss , Takis Konstantopoulos

Stochastic processes find applications in modelling systems in a variety of disciplines. A large number of stochastic models considered are Markovian in nature. It is often observed that higher order Markov processes can model the data…

Probability · Mathematics 2021-04-13 Suryadeepto Nag
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