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The usual setting for learning the structure and parameters of a graphical model assumes the availability of independent samples produced from the corresponding multivariate probability distribution. However, for many models the mixing time…

Machine Learning · Computer Science 2022-10-13 Arkopal Dutt , Andrey Y. Lokhov , Marc Vuffray , Sidhant Misra

We consider the Ising, and more generally, $q$-state Potts Glauber dynamics on random $d$-regular graphs on $n$ vertices at low temperatures $\beta \gtrsim \frac{\log d}{d}$. The mixing time is exponential in $n$ due to a bottleneck between…

Probability · Mathematics 2025-05-22 Reza Gheissari , Allan Sly , Youngtak Sohn

We give a new method for analysing the mixing time of a Markov chain using path coupling with stopping times. We apply this approach to two hypergraph problems. We show that the Glauber dynamics for independent sets in a hypergraph mixes…

Probability · Mathematics 2007-05-23 Magnus Bordewich , Martin Dyer , Marek Karpinski

Finding a ground state of a given Hamiltonian of an Ising model on a graph $G=(V,E)$ is an important but hard problem. The standard approach for this kind of problem is the application of algorithms that rely on single-spin-flip Markov…

Mixing of finite time-homogeneous Markov chains is well understood nowadays, with a rich set of techniques to estimate their mixing time. In this paper, we study the mixing time of random walks in dynamic random environments. To that end,…

Probability · Mathematics 2023-09-27 Raphael Erb

We give a survey of the known results on mixing time of Glauber dynamics for the Ising model on the square lattice and present a technique that makes exact sampling of the Ising model at all temperatures possible in polynomial time. At high…

Probability · Mathematics 2014-04-23 Mario Ullrich

A random walk is a basic stochastic process on graphs and a key primitive in the design of distributed algorithms. One of the most important features of random walks is that, under mild conditions, they converge to a stationary distribution…

Probability · Mathematics 2020-06-19 Leran Cai , Thomas Sauerwald , Luca Zanetti

We establish conditions on sequences of graphs which ensure that the mixing times of the random walks on the graphs in the sequence converge. The main assumption is that the graphs, associated measures and heat kernels converge in a…

Probability · Mathematics 2012-10-24 David Croydon , Ben Hambly , Takashi Kumagai

Masked language models (MLMs) define local conditional distributions over tokens but do not, in general, correspond to any consistent joint distribution over sequences. This raises a fundamental question: what global distributional behavior…

Machine Learning · Computer Science 2026-05-19 Suvadip Sana , Sami Wolf , Neer Mehta , Alina Shah , Aitzaz Shaikh , Janna Goodman , Lionel Levine

We give a new rapid mixing result for a natural random walk on the independent sets of a graph $G$. We show that when $G$ has bounded treewidth, this random walk -- known as the Glauber dynamics for the hardcore model -- mixes rapidly for…

Data Structures and Algorithms · Computer Science 2023-10-03 David Eppstein , Daniel Frishberg

We consider Glauber dynamics for the Ising model on the complete graph on $n$ vertices, known as the Curie-Weiss model. It is well-known that the mixing-time in the high temperature regime ($\beta < 1$) has order $n\log n$, whereas the…

Probability · Mathematics 2009-11-13 Jian Ding , Eyal Lubetzky , Yuval Peres

We study the mixing time of the single-site update Markov chain, known as the Glauber dynamics, for generating a random independent set of a tree. Our focus is obtaining optimal convergence results for arbitrary trees. We consider the more…

Discrete Mathematics · Computer Science 2025-03-05 Charilaos Efthymiou , Thomas P. Hayes , Daniel Stefankovic , Eric Vigoda

A popular method for sampling from high-dimensional distributions is the \emph{Gibbs sampler}, which iteratively resamples sites from the conditional distribution of the desired measure given the values of the other coordinates. It is…

Probability · Mathematics 2025-07-22 Jason Gaitonde , Elchanan Mossel

Real-world social and economic networks typically display a number of particular topological properties, such as a giant connected component, a broad degree distribution, the small-world property and the presence of communities of densely…

Disordered Systems and Neural Networks · Physics 2013-09-05 Diego Garlaschelli , Sebastian E. Ahnert , Thomas M. A. Fink , Guido Caldarelli

We study the ferromagnetic random field Ising model (RFIM) on a graph $G=(V,E)$ having maximal degree $\Delta$, where the external field at each vertex is an i.i.d. random variable. When the random field distribution is sufficiently…

Probability · Mathematics 2026-05-05 Yi Han

In this paper, the Glauber dynamics for the Ising model on the complete multipartite graph $K_{np_1,\dots,np_m}$ is investigated where $0<p_i<1$ is the proportion of the vertices in the $i$th component. We show that the dynamics exhibits…

Probability · Mathematics 2023-03-21 Heejune Kim

We address the convergence rate of Markov chains for randomly generating an edge coloring of a given tree. Our focus is on the Glauber dynamics which updates the color at a randomly chosen edge in each step. For a tree $T$ with $n$ vertices…

Discrete Mathematics · Computer Science 2024-07-08 Charlie Carlson , Xiaoyu Chen , Weiming Feng , Eric Vigoda

We study the mixing time of a Susceptible--Infected--Susceptible (SIS) model on graphs with external sources of infection, which we refer to as the noisy SIS model. Under suitable assumptions on the parameters of the dynamics, we show that…

Probability · Mathematics 2026-05-14 Wasiur R. KhudaBukhsh , Yangrui Xiang

The hard-core model has as its configurations the independent sets of some graph instance $G$. The probability distribution on independent sets is controlled by a `fugacity' $\lambda>0$, with higher $\lambda$ leading to denser…

Probability · Mathematics 2026-01-14 Mark Jerrum

Markov chain Monte Carlo (MCMC) methods are frequently used to approximately simulate high-dimensional, multimodal probability distributions. In adaptive MCMC methods, the transition kernel is changed "on the fly" in the hope to speed up…

Probability · Mathematics 2014-06-04 Winfried Barta