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The problem of sampling from the stationary distribution of a Markov chain finds widespread applications in a variety of fields. The time required for a Markov chain to converge to its stationary distribution is known as the classical…

Quantum Physics · Physics 2022-09-14 Shantanav Chakraborty , Kyle Luh , Jérémie Roland

We prove a theorem that reduces bounding the mixing time of a card shuffle to verifying a condition that involves only triplets of cards. Then we use it to analyze a classic model of card shuffling. In 1988, Diaconis introduced the…

Probability · Mathematics 2024-11-12 Olena Blumberg , Ben Morris , Alto Senda

A 1-2 model configuration is a subset of edges of a hexagonal lattice satisfying the constraint that each vertex is incident to 1 or 2 edges. We introduce Markov chains to sample the 1-2 model configurations on 2D hexagonal lattice and…

Probability · Mathematics 2019-01-01 Zhongyang Li

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

The random-cluster model is a unifying framework for studying random graphs, spin systems and electrical networks that plays a fundamental role in designing efficient Markov Chain Monte Carlo (MCMC) sampling algorithms for the classical…

Probability · Mathematics 2023-08-30 Antonio Blanca , Alistair Sinclair , Xusheng Zhang

We establish tight results for rapid mixing of Gibbs samplers for the Ferromagnetic Ising model on general graphs. We show that if \[(d-1)\tanh\beta<1,\] then there exists a constant C such that the discrete time mixing time of Gibbs…

Probability · Mathematics 2013-02-06 Elchanan Mossel , Allan Sly

We study the effect of boundary conditions on the relaxation time of the Glauber dynamics for the hard-core model on the tree. The hard-core model is defined on the set of independent sets weighted by a parameter $\lambda$, called the…

Probability · Mathematics 2010-07-15 Ricardo Restrepo , Daniel Stefankovic , Juan C. Vera , Eric Vigoda , Linji Yang

The spectral independence approach of Anari et al. (2020) utilized recent results on high-dimensional expanders of Alev and Lau (2020) and established rapid mixing of the Glauber dynamics for the hard-core model defined on weighted…

Data Structures and Algorithms · Computer Science 2020-07-17 Zongchen Chen , Andreas Galanis , Daniel Štefankovič , Eric Vigoda

We introduce and analyze a natural class of nonlinear dynamics for spin systems such as the Ising model. This class of dynamics is based on the framework of mass action kinetics, which models the evolution of systems of entities under…

Probability · Mathematics 2024-12-24 Pietro Caputo , Alistair Sinclair

We give conditions under which a Markov chain constructed via parallel or simulated tempering is guaranteed to be rapidly mixing, which are applicable to a wide range of multimodal distributions arising in Bayesian statistical inference and…

Probability · Mathematics 2009-06-15 Dawn B. Woodard , Scott C. Schmidler , Mark Huber

We give a short proof that low-temperature dynamics for the Sherrington-Kirkpatrick model have mixing time exponential in the system size, based on the recently proved existence of gapped spin configurations by (Minzer-Sah-Sawhney 2023,…

Probability · Mathematics 2025-12-10 Mark Sellke

Two recent and seemingly-unrelated techniques for proving mixing bounds for Markov chains are: (i) the framework of Spectral Independence, introduced by Anari, Liu and Oveis Gharan, and its numerous extensions, which have given rise to…

Probability · Mathematics 2022-06-07 Yuansi Chen , Ronen Eldan

We study the mixing time of Metropolis-Adjusted Langevin algorithm (MALA) for sampling a target density on $\mathbb{R}^d$. We assume that the target density satisfies $\psi_\mu$-isoperimetry and that the operator norm and trace of its…

Machine Learning · Statistics 2023-06-09 Yuansi Chen , Khashayar Gatmiry

Providing evidence that quantum computers can efficiently prepare low-energy or thermal states of physically relevant interacting quantum systems is a major challenge in quantum information science. A newly developed quantum Gibbs sampling…

Quantum Physics · Physics 2024-11-08 Akshar Ramkumar , Mehdi Soleimanifar

This is an expository paper, focussing on the following scenario. We have two Markov chains, $\mathcal {M}$ and $\mathcal {M}'$. By some means, we have obtained a bound on the mixing time of $\mathcal {M}'$. We wish to compare $\mathcal…

Probability · Mathematics 2007-05-23 Martin Dyer , Leslie Ann Goldberg , Mark Jerrum , Russell Martin

The edge flipping is a non-reversible Markov chain on a given connected graph, which is defined by Chung and Graham. In the same paper, its eigenvalues and stationary distributions for some classes of graphs are identified. We further study…

Probability · Mathematics 2022-09-07 Yunus Emre Demirci , Ümit Işlak , Alperen Özdemir

We analyse the metastable behaviour of the dilute Curie-Weiss model subject to a Glauber dynamics. The model is a random version of a mean-field Ising model, where the coupling coefficients are Bernoulli random variables with mean $p\in…

Probability · Mathematics 2021-04-26 Anton Bovier , Saeda Marello , Elena Pulvirenti

Motivated by the `subgraphs world' view of the ferromagnetic Ising model, we develop a general approach to studying mixing times of Glauber dynamics based on subset expansion expressions for a class of graph polynomials. With a canonical…

Combinatorics · Mathematics 2015-10-29 Magnus Bordewich , Ross J. Kang

We study the theoretical complexity of simulated tempering for sampling from mixtures of log-concave components differing only by location shifts. The main result establishes the first polynomial-time guarantee for simulated tempering…

Computation · Statistics 2025-11-04 Quan Zhou

We consider stochastic optimization problems where data is drawn from a Markov chain. Existing methods for this setting crucially rely on knowing the mixing time of the chain, which in real-world applications is usually unknown. We propose…

Machine Learning · Computer Science 2023-07-14 Ron Dorfman , Kfir Y. Levy