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We prove an upper bound on the total variation mixing time of a finite Markov chain in terms of the absolute spectral gap and the number of elements in the state space. Unlike results requiring reversibility or irreducibility, this bound is…

Probability · Mathematics 2013-10-31 Daniel Jerison

In this paper, we develop a general theory for the estimation of the transition probabilities of reversible Markov chains using the maximum entropy principle. A broad range of physical models can be studied within this approach. We use…

Statistical Mechanics · Physics 2015-05-14 Erik Van der Straeten

Irreversibility is commonly quantified by entropy production. An external observer can estimate it through measuring an observable that is antisymmetric under time-reversal like a current. We introduce a general framework that, inter alia,…

Statistical Mechanics · Physics 2023-07-05 Jann van der Meer , Julius Degünther , Udo Seifert

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

We suggest an approach to obtaining general two-sided bounds on the rate of convergence in terms of special "weighted" norms related to total variation. Some important classes of continuous-time Markov chains are considered:…

Probability · Mathematics 2015-07-15 A. Zeifman , V. Korolev

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

We establish convergence to an invariant measure as time tends to infinity, for a large class of (possibly non-Markovian) stochastic volatility models. Our arguments are based on a novel coupling idea for Markov chains which also extends to…

Probability · Mathematics 2021-08-30 Balázs Gerencsér , Miklós Rásonyi

We investigate the mixing properties of a finite Markov chain in random environment defined as a mixture of a deterministic chain and a chain whose state space has been permuted uniformly at random. This work is the counterpart of a…

Probability · Mathematics 2024-02-07 Bastien Dubail

Motivated by robotic surveillance applications, this paper studies the novel problem of maximizing the return time entropy of a Markov chain, subject to a graph topology with travel times and stationary distribution. The return time entropy…

Optimization and Control · Mathematics 2018-05-29 Xiaoming Duan , Mishel George , Francesco Bullo

An aperiodic and irreducible Markov chain on a finite state space converges to its stationary distribution. When convergence to equilibrium is measured by total variation distance, there exists an optimal coupling and a maximal coupling…

Probability · Mathematics 2015-04-01 Agnes Coquio

This article provides the first procedure for computing a fully data-dependent interval that traps the mixing time $t_{\text{mix}}$ of a finite reversible ergodic Markov chain at a prescribed confidence level. The interval is computed from…

Machine Learning · Computer Science 2015-11-04 Daniel Hsu , Aryeh Kontorovich , Csaba Szepesvári

Let $(\xi_n)_{n=0}^\infty$ be a nonhomogeneous Markov chain taking values from finite state-space of $\mathbf{X}=\{1,2,\ldots,b\}$. In this paper, we will study the generalized entropy ergodic theorem with almost-everywhere and…

Probability · Mathematics 2015-01-19 Zhongzhi Wang , Weiguo Yang

We construct a new Markov chain Monte Carlo method on finite states with optimal choices of acceptance-rejection ratio functions. We prove that the constructed continuous time Markov jumping process has a global in-time convergence rate in…

Optimization and Control · Mathematics 2023-02-06 Wuchen Li , Linyuan Lu

We give computable bounds on the rate of convergence of the transition probabilities to the stationary distribution for a certain class of geometrically ergodic Markov chains. Our results are different from earlier estimates of Meyn and…

Probability · Mathematics 2007-05-23 Peter H. Baxendale

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

We study finite-time spectral rigidity in reversible Markov chains via exact spectral relaxation dynamics. While the underlying identities follow classically from self-adjointness on $L^2(\pi)$, organizing the dynamics around the relaxation…

Probability · Mathematics 2026-05-19 Qiao Wang

We consider continuous-time Markov chain on a finite state space X. We assume X can be clustered into several subsets such that the intra-transition rates within these subsets are of order $\mathcal{O}(\frac{1}{\epsilon})$ comparing to the…

Probability · Mathematics 2016-01-28 Wei Zhang

The spectral gap $\gamma$ of a finite, ergodic, and reversible Markov chain is an important parameter measuring the asymptotic rate of convergence. In applications, the transition matrix $P$ may be unknown, yet one sample of the chain up to…

Statistics Theory · Mathematics 2017-08-25 Daniel Hsu , Aryeh Kontorovich , David A. Levin , Yuval Peres , Csaba Szepesvári

We address the problem of estimating the mixing time $t_{\mathsf{mix}}$ of an arbitrary ergodic finite-state Markov chain from a single trajectory of length $m$. The reversible case was addressed by Hsu et al. [2019], who left the general…

Statistics Theory · Mathematics 2022-08-17 Geoffrey Wolfer , Aryeh Kontorovich

This paper integrates two strands of the literature on stability of general state Markov chains: conventional, total variation based results and more recent order-theoretic results. First we introduce a complete metric over Borel…

Probability · Mathematics 2024-10-02 Takashi Kamihigashi , John Stachurski
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