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Markov random fields provide a compact representation of joint probability distributions by representing its independence properties in an undirected graph. The well-known Hammersley-Clifford theorem uses these conditional independences to…

Artificial Intelligence · Computer Science 2013-06-12 Alejandro Edera , Facundo Bromberg , Federico Schlüter

We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more general exponential models. This result…

Artificial Intelligence · Computer Science 2013-01-07 Dan Geiger , Christopher Meek , Bernd Sturmfels

We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more general exponential models. For decomposable…

Statistics Theory · Mathematics 2007-06-13 Dan Geiger , Christopher Meek , Bernd Sturmfels

The Hammersley-Clifford theorem states that if the support of a Markov random field has a safe symbol then it is a Gibbs state with some nearest neighbour interaction. In this paper we generalise the theorem with an added condition that the…

Probability · Mathematics 2015-12-01 Nishant Chandgotia

Lattice Conditional Independence models are a class of models developed first for the Gaussian case in which a distributive lattice classifies all the conditional independence statements. The main result is that these models can…

Statistics Theory · Mathematics 2022-06-14 Peter Caines , Fatemeh Mohammadi , Eduardo Sáenz de Cabezón , Henry Wynn

The problem of inferring the distribution of a random vector given that its norm is large requires modeling a homogeneous limiting density. We suggest an approach based on graphical models which is suitable for high-dimensional vectors. We…

Probability · Mathematics 2022-12-20 Adrien Hitz , Robin Evans

We outline the theory of sets with distributive operations: multishelves and multispindles, with examples provided by semi-lattices, lattices and skew lattices. For every such a structure we define multi-term distributive homology and show…

Geometric Topology · Mathematics 2013-12-17 Jozef H. Przytycki , Krzysztof K. Putyra

Conditional independence and graphical models are well studied for probability distributions on product spaces. We propose a new notion of conditional independence for any measure $\Lambda$ on the punctured Euclidean space $\mathbb…

Statistics Theory · Mathematics 2024-09-12 Sebastian Engelke , Jevgenijs Ivanovs , Kirstin Strokorb

Conditional independence, graphical models and sparsity are key notions for parsimonious statistical models and for understanding the structural relationships in the data. The theory of multivariate and spatial extremes describes the risk…

Statistics Theory · Mathematics 2019-11-14 Sebastian Engelke , Adrien S. Hitz

Let $L$ be a finite lattice and let $I$ be an ideal of $L$. Then the restriction map is a bounded lattice homomorphism of the congruence lattice of~$L$ into the congruence lattice of $I$. In a 2009 paper, the authors proved the converse. In…

Rings and Algebras · Mathematics 2022-01-11 George Grätzer , Harry Lakser

We show that all finite lattices, including non-distributive lattices, arise as stable matching lattices when all agents have path-independent choice functions. This result answers an open question of Blair~\cite{blair1988lattice}. In the…

Discrete Mathematics · Computer Science 2026-04-09 Christopher En , Yuri Faenza

This paper considers the problem of defining distributions over graphical structures. We propose an extension of the hyper Markov properties of Dawid and Lauritzen [Ann. Statist. 21 (1993) 1272-1317], which we term structural Markov…

Statistics Theory · Mathematics 2020-04-28 Simon Byrne , A. Philip Dawid

The Central Limit Theorem states that, in the limit of a large number of terms, an appropriately scaled sum of independent random variables yields another random variable whose probability distribution tends to a stable distribution. The…

Data Analysis, Statistics and Probability · Physics 2024-04-08 Damián H. Zanette , Inés Samengo

Dilworth's theorem. Every finite distributive lattice $D$ can be represented as the congruence lattice of a finite lattice $L$. We want: Every finite distributive lattice $D$ can be represented as the congruence lattice of a nice finite…

Rings and Algebras · Mathematics 2013-10-01 George Grätzer

The theory of dependency graphs is a powerful toolbox to prove asymptotic normality of sums of random variables. In this article, we introduce a more general notion of weighted dependency graphs and give normality criteria in this context.…

Probability · Mathematics 2018-10-18 Valentin Féray

We establish a central limit theorem for the sum of $\epsilon$-independent random variables, extending both the classical and free probability setting. Central to our approach is the use of graphon limits to characterize the limiting…

Probability · Mathematics 2024-12-02 Guillaume Cébron , Patrick Oliveira Santos , Pierre Youssef

For a uniform random labelled tree, we find the limiting distribution of tree parameters which are stable (in some sense) with respect to local perturbations of the tree structure. The proof is based on the martingale central limit theorem…

Combinatorics · Mathematics 2022-06-16 Mikhail Isaev , Angus Southwell , Maksim Zhukovskii

Birkhoff's representation theorem for finite distributive lattices states that any finite distributive lattice is isomorphic to the lattice of order ideals (lower sets) of the partial order of the join-irreducible elements of the lattice.…

Combinatorics · Mathematics 2023-03-15 Dale R. Worley

A Lattice is a partially ordered set where both least upper bound and greatest lower bound of any pair of elements are unique and exist within the set. K\"{o}tter and Kschischang proved that codes in the linear lattice can be used for error…

Discrete Mathematics · Computer Science 2021-09-30 Pranab Basu

With a sequence of regressions, one may generate joint probability distributions. One starts with a joint, marginal distribution of context variables having possibly a concentration graph structure and continues with an ordered sequence of…

Statistics Theory · Mathematics 2017-02-03 Kayvan Sadeghi , Nanny Wermuth
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