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In this paper we study the central limit theorem for additive functionals of stationary Markov chains with general state space by using a new idea involving conditioning with respect to both the past and future of the chain. Practically, we…

Probability · Mathematics 2020-05-19 Magda Peligrad

The aim of this note is to present an elementary proof of a variation of Harris' ergodic theorem of Markov chains. This theorem, dating back to the fifties essentially states that a Markov chain is uniquely ergodic if it admits a ``small''…

Probability · Mathematics 2008-10-16 Martin Hairer , Jonathan C. Mattingly

General Markov chains with a countably additive transition probability in arbitrary phase space are considered. Markov operators extend from the space of countably additive measures to the space of finitely additive measures. In the…

Probability · Mathematics 2018-04-10 Alexander I. Zhdanok

The theory of $L^2$-spectral gaps for reversible Markov chains has been studied by many authors. In this paper we consider positive recurrent general state space Markov chains with stationary transition probabilities. Replacing the…

Probability · Mathematics 2009-08-07 Achim Wuebker , Zakhar Kabluchko

We define a conjugate prior for the reversible Markov chain of order $r$. The prior arises from a partially exchangeable reinforced random walk, in the same way that the Beta distribution arises from the exchangeable Poly\'{a} urn. An…

Statistics Theory · Mathematics 2011-05-16 Sergio Bacallado

A stable-like Markov chain is a time-homogeneous Markov chain on the real line with the transition kernel $p(x,dy)=f_x(y-x)dy$, where the density functions $f_x(y)$, for large $|y|$, have a power-law decay with exponent $\alpha(x)+1$, where…

Probability · Mathematics 2014-12-01 Nikola Sandrić

Consider a sequence $(\eta^N(t) :t\ge 0)$ of continuous-time, irreducible Markov chains evolving on a fixed finite set $E$, indexed by a parameter $N$. Denote by $R_N(\eta,\xi)$ the jump rates of the Markov chain $\eta^N_t$, and assume that…

Probability · Mathematics 2015-12-22 C. Landim , T. Xu

Via operator theoretic methods, we formalize the concentration phenomenon for a given observable `$r$' of a discrete time Markov chain with `$\mu_{\pi}$' as invariant ergodic measure, possibly having support on an unbounded state space. The…

Machine Learning · Computer Science 2023-06-01 Muhammad Abdullah Naeem , Miroslav Pajic

Consider an ergodic Markov chain on a countable state space for which the return times have exponential tails. We show that the stationary version of any such chain is a finitary factor of an i.i.d. process. A key step is to show that any…

Probability · Mathematics 2023-06-22 Omer Angel , Yinon Spinka

Verification of infinite-state Markov chains is still a challenge despite several fruitful numerical or statistical approaches. For decisive Markov chains, there is a simple numerical algorithm that frames the reachability probability as…

Logic in Computer Science · Computer Science 2024-09-30 Benoît Barbot , Patricia Bouyer , Serge Haddad

We consider a discrete-time Markov chain $(X^t,Y^t)$, $t=0,1,2,...$, where the $X$-component forms a Markov chain itself. Assume that $(X^t)$ is Harris-ergodic and consider an auxiliary Markov chain ${\hat{Y}^t}$ whose transition…

Probability · Mathematics 2013-02-13 Sergey Foss , Seva Shneer , Andrey Tyurlikov

We analyze a stochastic process resulting from the normalization of states in the zeroth-order optimization method CMA-ES. On a specific class of minimization problems where the objective function is scaling-invariant, this process defines…

Optimization and Control · Mathematics 2024-10-02 Armand Gissler , Shan-Conrad Wolf , Anne Auger , Nikolaus Hansen

Predictive constructions are a powerful way of characterizing the probability law of stochastic processes with certain forms of invariance, such as exchangeability or Markov exchangeability. When de Finetti-like representation theorems are…

Methodology · Statistics 2015-11-16 Sandra Fortini , Sonia Petrone

This paper proposes a new type of recurrence where we divide the Markov chains into intervals that start when the chain enters into a subset A, then sample another subset B far away from A and end when the chain again return to A. The…

Methodology · Statistics 2016-02-24 Lars Holden

We consider a family of Markov chains whose transition dynamics are affected by model parameters. Understanding the parametric dependence of (complex) performance measures of such Markov chains is often of significant interest. The…

Probability · Mathematics 2017-07-14 Chang-Han Rhee , Peter Glynn

Given an infinitesimal perturbation of a discrete-time finite Markov chain, we seek the states that are stable despite the perturbation, \textit{i.e.} the states whose weights in the stationary distributions can be bounded away from $0$ as…

Discrete Mathematics · Computer Science 2016-02-15 Volker Betz , Stephane Le Roux

We study a large class of reversible Markov chains with discrete state space and transition matrix $P_N$. We define the notion of a set of {\it metastable points} as a subset of the state space $\G_N$ such that (i) this set is reached from…

Probability · Mathematics 2007-05-23 A. Bovier , M. Eckhoff , V. Gayrard , M. Klein

We consider periodic Markov chains with absorption. Applying to iterates of this periodic Markov chain criteria for the exponential convergence of conditional distributions of aperiodic absorbed Markov chains, we obtain exponential…

Probability · Mathematics 2022-11-08 Nicolas Champagnat , Denis Villemonais

We present a new algorithm for the statistical model checking of Markov chains with respect to unbounded temporal properties, such as reachability and full linear temporal logic. The main idea is that we monitor each simulation run on the…

Logic in Computer Science · Computer Science 2016-03-04 Przemysław Daca , Thomas A. Henzinger , Jan Křetínský , Tatjana Petrov

When the initial and transition probabilities of a finite Markov chain in discrete time are not well known, we should perform a sensitivity analysis. This is done by considering as basic uncertainty models the so-called credal sets that…

Artificial Intelligence · Computer Science 2014-08-12 Gert de Cooman , Filip Hermans , Erik Quaeghebeur