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We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on $\ell_1$-regularized logistic regression, in which the neighborhood of any given node is estimated by…

Statistics Theory · Mathematics 2010-10-05 Pradeep Ravikumar , Martin J. Wainwright , John D. Lafferty

We consider the problem of model selection in Gaussian Markov fields in the sample deficient scenario. The benchmark information-theoretic results in the case of d-regular graphs require the number of samples to be at least proportional to…

Machine Learning · Statistics 2018-03-30 Ilya Soloveychik , Vahid Tarokh

A solution to a problem of Erd\H{o}s, Rubin and Taylor is obtained by showing that if a graph $G$ is $(a:b)$-choosable, and $c/d > a/b$, then $G$ is not necessarily $(c:d)$-choosable. The simplest case of another problem, stated by the same…

Discrete Mathematics · Computer Science 2008-02-18 Shai Gutner

We consider the problem of estimating the graph structure associated with a discrete Markov random field. We describe a method based on $\ell_1$-regularized logistic regression, in which the neighborhood of any given node is estimated by…

Statistics Theory · Mathematics 2008-04-29 Pradeep Ravikumar , Martin J. Wainwright , John D. Lafferty

We consider the problem of inferring the conditional independence graph (CIG) of a multivariate stationary dicrete-time Gaussian random process based on a finite length observation. Using information-theoretic methods, we derive a lower…

Statistics Theory · Mathematics 2014-03-06 Gabor Hannak , Alexander Jung , Norbert Goertz

A solution to a problem of Erd\H{o}s, Rubin and Taylor is obtained by showing that if a graph $G$ is $(a:b)$-choosable, and $c/d > a/b$, then $G$ is not necessarily $(c:d)$-choosable. Applying probabilistic methods, an upper bound for the…

Discrete Mathematics · Computer Science 2008-02-12 Shai Gutner , Michael Tarsi

Directed and undirected graphical models, also called Bayesian networks and Markov random fields, respectively, are important statistical tools in a wide variety of fields, ranging from computational biology to probabilistic artificial…

Combinatorics · Mathematics 2007-06-13 Sergi Elizalde , Kevin Woods

Markov random fields area popular model for high-dimensional probability distributions. Over the years, many mathematical, statistical and algorithmic problems on them have been studied. Until recently, the only known algorithms for…

Machine Learning · Computer Science 2017-06-01 Linus Hamilton , Frederic Koehler , Ankur Moitra

In this paper, we study the concept of edge-group choosability of graphs. We say that G is edge k-group choosable if its line graph is k-group choosable. An edge-group choosability version of Vizing conjecture is given. The evidence of our…

Combinatorics · Mathematics 2011-06-07 Amir Khamseh , Gholamreza Omidi

We give a sharp bound on the number of triangles in a graph with fixed number of edges. We also characterize graphs that achieve the maximum number of triangles. Using the upper bound on number of triangles, we prove that if $G$ is a…

Group Theory · Mathematics 2022-05-13 Tony N. Mavely , Viji Z. Thomas

We consider the problem of estimating the parameters in a pairwise graphical model in which the distribution of each node, conditioned on the others, may have a different parametric form. In particular, we assume that each node's…

Methodology · Statistics 2014-08-05 Shizhe Chen , Daniela Witten , Ali Shojaie

In this work, we propose a global model selection criterion to estimate the graph of conditional dependencies of a random vector based on a finite sample. By global criterion, we mean optimizing a function over the entire set of possible…

Statistics Theory · Mathematics 2023-11-06 Florencia Leonardi , Magno T. F Severino

We consider the problem of list edge coloring for planar graphs. Edge coloring is the problem of coloring the edges while ensuring that two edges that are incident receive different colors. A graph is k-edge-choosable if for any assignment…

Discrete Mathematics · Computer Science 2013-03-19 Marthe Bonamy

We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test…

Machine Learning · Statistics 2014-02-05 Rui Wu , R. Srikant , Jian Ni

Markov chains are convenient means of generating realizations of networks with a given (joint or otherwise) degree distribution, since they simply require a procedure for rewiring edges. The major challenge is to find the right number of…

Social and Information Networks · Computer Science 2012-11-01 J. Ray , A. Pinar , C. Seshadhri

We consider the problem of decomposing the edges of a directed graph into as few paths as possible. There is a natural lower bound for the number of paths needed in an edge decomposition of a directed graph $D$ in terms of its degree…

Combinatorics · Mathematics 2021-09-29 Alberto Espuny Díaz , Viresh Patel , Fabian Stroh

In this paper, we study the two choice balls and bins process when balls are not allowed to choose any two random bins, but only bins that are connected by an edge in an underlying graph. We show that for $n$ balls and $n$ bins, if the…

Data Structures and Algorithms · Computer Science 2007-05-23 K. Kenthapadi , R. Panigrahy

We study the problem of detecting local geometry in random graphs. We introduce a model $\mathcal{G}(n, p, d, k)$, where a hidden community of average size $k$ has edges drawn as a random geometric graph on $\mathbb{S}^{d-1}$, while all…

Statistics Theory · Mathematics 2026-03-26 Jinho Bok , Shuangping Li , Sophie H. Yu

We study Markov chains for randomly sampling $k$-colorings of a graph with maximum degree $\Delta$. Our main result is a polynomial upper bound on the mixing time of the single-site update chain known as the Glauber dynamics for planar…

Probability · Mathematics 2011-09-01 Thomas P. Hayes , Juan C. Vera , Eric Vigoda

Given a graphical degree sequence ${\bf d}=(d_1,\ldots, d_n)$, let $G(n, {\bf d})$ denote a uniformly random graph on vertex set $[n]$ where vertex $ i$ has degree $d_i$ for every $1\le i\le n$. We give upper and lower bounds on the joint…

Combinatorics · Mathematics 2025-05-28 Pu Gao , Yuval Ohapkin
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