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A class of examples is constructed to show that for strictly stationary Markov chains that are reversible, the simultaneous mixing rates for the $\rho$-mixing and strong mixing ($\alpha$-mixing) conditions can be fairly arbitrary, within…

Probability · Mathematics 2022-10-04 Richard C. Bradley

We study the spectral gaps of parallel and simulated tempering chains targeting multimodal Gibbs measures. In particular, we consider chains constructed from Metropolis random walks that preserve the Gibbs distributions at a sequence of…

Probability · Mathematics 2026-04-07 Seungjae Son

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 propose a new definition of metastability of Markov processes on countable state spaces. We obtain sufficient conditions for a sequence of processes to be metastable. In the reversible case these conditions are expressed in terms of the…

Probability · Mathematics 2015-05-14 Johel Beltrán , Claudio Landim

It is known that state-dependent, multi-step Lyapunov bounds lead to greatly simplified verification theorems for stability for large classes of Markov chain models. This is one component of the "fluid model" approach to stability of…

Optimization and Control · Mathematics 2012-05-18 Serdar Yüksel , Sean P. Meyn

Perturbation theory for Markov chains addresses the question how small differences in the transitions of Markov chains are reflected in differences between their distributions. We prove powerful and flexible bounds on the distance of the…

Computation · Statistics 2017-02-27 Daniel Rudolf , Nikolaus Schweizer

In this paper we consider Markov chains with transition rates that depend on a small parameter $\varepsilon$. Under a mild assumption on the asymptotics of these transition rates, we describe the behavior of the chain at various…

Probability · Mathematics 2017-04-26 Mark Freidlin , Leonid Koralov

In this paper we study Markov chains associated with the Metropolis-Hastings algorithm. We consider conditions under which the sequence of the successive densities of such a chain converges to the target density according to the total…

Statistics Theory · Mathematics 2020-06-16 Dimiter Tsvetkov , Lyubomir Hristov , Ralitsa Angelova-Slavova

We investigate the metastable behavior of reversible Markov chains on possibly countable infinite state spaces. Based on a new definition of metastable Markov processes, we compute precisely the mean transition time between metastable sets.…

Probability · Mathematics 2020-01-08 André Schlichting , Martin Slowik

Consider the complete graph on $n$ vertices. To each vertex assign an Ising spin that can take the values $-1$ or $+1$. Each spin $i \in [n]=\{1,2,\dots, n\}$ interacts with a magnetic field $h \in [0,\infty)$, while each pair of spins $i,j…

Probability · Mathematics 2022-04-28 Anton Bovier , Frank den Hollander , Saeda Marello

In this paper we are concerned with hitting times of a family of density-dependent Markov chains. A moderate deviation principle of the hitting time is given. The proof of the main theorem relies heavily on moderate deviations of…

Probability · Mathematics 2022-06-15 Yuheng He , Xiaofeng Xue

We study a class of Markov chains that describe reversible stochastic dynamics of a large class of disordered mean field models at low temperatures. Our main purpose is to give a precise relation between the metastable time scales in the…

Disordered Systems and Neural Networks · Physics 2016-08-31 A. Bovier , M. Eckhoff , V. Gayrard , M. Klein

For a stochastically monotone Markov chain taking values in a Polish space, we present a number of conditions for existence and for uniqueness of its stationary regime, as well as for closeness of its transient trajectories. In particular,…

Probability · Mathematics 2026-04-02 Sergey Foss , Michael Scheutzow

In this article, we study the mixing properties of metastable diffusion processes which possess a Gibbs invariant distribution. For systems with multiple stable equilibria, so-called metastable transitions between these equilibria are…

Probability · Mathematics 2025-04-29 Jungkyoung Lee

In this letter we announce rigorous results that elucidate the relation between metastable states and low-lying eigenvalues in Markov chains in a much more general setting and with considerable greater precision as was so far available.…

Disordered Systems and Neural Networks · Physics 2009-10-31 A. Bovier , M. Eckhoff , V. Gayrard , M. Klein

The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a perturbed Metropolis-Hastings (MH) algorithm, provides an approach for approximate sampling when the target distribution is intractable. Assuming the unperturbed Markov…

Computation · Statistics 2019-07-31 Felipe Medina-Aguayo , Daniel Rudolf , Nikolaus Schweizer

Convergence rate analyses of random walk Metropolis-Hastings Markov chains on general state spaces have largely focused on establishing sufficient conditions for geometric ergodicity or on analysis of mixing times. Geometric ergodicity is a…

Statistics Theory · Mathematics 2023-07-24 Riddhiman Bhattacharya , Galin L. Jones

We consider the problem of metastability for a stochastic dynamics with a parallel updating rule with single spin rates equal to those of the heat bath for the Ising nearest neighbors interaction. We study the exit from the metastable…

Statistical Mechanics · Physics 2009-07-14 Emilio N. M. Cirillo , Francesca R. Nardi

We introduce a general class of mean-field-like spin systems with random couplings that comprises both the Ising model on inhomogeneous dense random graphs and the randomly diluted Hopfield model. We are interested in quantitative estimates…

Probability · Mathematics 2024-07-10 Anton Bovier , Frank den Hollander , Saeda Marello , Elena Pulvirenti , Martin Slowik

Physically motivated stochastic dynamics are often used to sample from high-dimensional distributions. However such dynamics often get stuck in specific regions of their state space and mix very slowly to the desired stationary state. This…

Machine Learning · Statistics 2025-05-13 Abhijith Jayakumar , Andrey Y. Lokhov , Sidhant Misra , Marc Vuffray