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We study the spatial Gibbs random graphs introduced in [MV16] from the point of view of local convergence. These are random graphs embedded in an ambient space consisting of a line segment, defined through a probability measure that favors…

Probability · Mathematics 2017-12-12 Eric Ossami Endo , Daniel Valesin

We study the Glauber dynamics of Ising spin models with random bonds, on finitely connected random graphs. We generalize a recent dynamical replica theory with which to predict the evolution of the joint spin-field distribution, to include…

Disordered Systems and Neural Networks · Physics 2015-05-13 A. Mozeika , A. C. C. Coolen

We consider Bose--Einstein condensation (BEC) on graphs with transient adjacency matrix and obtain a quasi-free state exhibiting BEC is non-factor and decompose into generalized coherent states. We review necessary and sufficient conditions…

Mathematical Physics · Physics 2017-07-19 Tomohiro Kanda

We apply the cavity method to a spin glass model on a `small world' lattice, a random bond graph super-imposed upon a 1-dimensional ferromagnetic ring. We show the correspondence with a replicated transfer matrix approach, up to the level…

Disordered Systems and Neural Networks · Physics 2015-06-24 B Wemmenhove , T Nikoletopoulos , J P L Hatchett

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables…

Methodology · Statistics 2013-01-14 Jared S. Murray , David B. Dunson , Lawrence Carin , Joseph E. Lucas

Bayesian inference for graphical models has received much attention in the literature in recent years. It is well known that when the graph G is decomposable, Bayesian inference is significantly more tractable than in the general…

Methodology · Statistics 2015-05-05 Kshitij Khare , Bala Rajaratnam , Abhishek Saha

Dependency networks (Heckerman et al., 2000) provide a flexible framework for modeling complex systems with many variables by combining independently learned local conditional distributions through pseudo-Gibbs sampling. Despite their…

Machine Learning · Computer Science 2026-04-02 Kazuya Takabatake , Shotaro Akaho

In a number of previous studies, we have investigated the use of the volume element of the Bures (minimal monotone) metric -- identically, one-fourth of the statistical distinguishability (SD) metric -- as a natural measure over the…

Quantum Physics · Physics 2007-05-23 Paul B. Slater

We discuss the concept of Pure State of the Replica Symmetry Breaking ansatz in finite and infinite spin systems without averaging on the disorder, nor using replicas. Consider a system of n spins $\sigma\in\Omega^{n}$ with the usual set…

Statistical Mechanics · Physics 2025-08-06 Simone Franchini

Gaussian boson sampling (GBS) is a promising protocol for demonstrating quantum computational advantage. One of the key steps for proving classical hardness of GBS is the so-called ``hiding conjecture'', which asserts that one can ``hide''…

Quantum Physics · Physics 2025-09-03 Laura Shou , Sarah H. Miller , Victor Galitski

Belief propagation (BP) is a powerful tool to solve distributed inference problems, though it is limited by short cycles in the corresponding factor graph. Such cycles may lead to incorrect solutions or oscillatory behavior. Only for…

Systems and Control · Computer Science 2018-02-08 Christopher Lindberg , Julien M. Hendrickx , Henk Wymeersch

We work in a general framework where the state of a physical system is defined by its behaviour under measurement and the global state is constrained by no-signalling conditions. We show that the marginals of symmetric states in such…

Quantum Physics · Physics 2009-04-16 Matthias Christandl , Ben Toner

We study a class of Gibbs measures of classical particle spin systems with spin space $S=\mathbb{R}^{m}$ and unbounded pair interaction, living on a metric graph given by a typical realization $\gamma $ of a random point process in…

Mathematical Physics · Physics 2015-06-16 Alexei Daletskii , Yuri Kondratiev , Yuri Kozitsky , Tanja Pasurek

We develop a method for the random sampling of (multimode) Gaussian states in terms of their covariance matrix, which we refer to as a random quantum covariance matrix (RQCM). We analyze the distribution of marginals and demonstrate that…

Quantum Physics · Physics 2024-08-19 Leevi Leppäjärvi , Ion Nechita , Ritabrata Sengupta

An important question in statistical network analysis is how to estimate models of discrete and dependent network data with intractable likelihood functions, without sacrificing computational scalability and statistical guarantees. We…

Statistics Theory · Mathematics 2026-03-06 Jonathan R. Stewart , Michael Schweinberger

The Bethe ansatz represents an analytical method enabling the exact solution of numerous models in condensed matter physics and statistical mechanics. When a global symmetry is present, the trial wavefunctions of the Bethe ansatz consist of…

Quantum Physics · Physics 2024-05-24 Roberto Ruiz , Alejandro Sopena , Max Hunter Gordon , Germán Sierra , Esperanza López

Continuing our earlier work in \cite{nss20a}, we study the random regular k-NAE-SAT model in the condensation regime. In \cite{nss20a}, the 1RSB properties of the model were established with positive probability. In this paper, we improve…

Probability · Mathematics 2023-12-19 Danny Nam , Allan Sly , Youngtak Sohn

Gibbs states of an infinite system of interacting quantum particles are considered. Each particle moves on a compact Riemannian manifold and is attached to a vertex of a graph (one particle per vertex). Two kinds of graphs are studied: (a)…

Mathematical Physics · Physics 2009-11-11 D. Kepa , Y. Kozitsky

Wigner-positive quantum states have the peculiarity to admit a Wigner function that is a genuine probability distribution over phase space. The Shannon differential entropy of the Wigner function of such states -- called Wigner entropy for…

Quantum Physics · Physics 2026-01-27 Zacharie Van Herstraeten , Nicolas J. Cerf

Split conformal prediction provides finite-sample marginal coverage under exchangeability, but this guarantee averages over the random calibration sample. We study instead the law of the calibration-conditional coverage induced by a…

Machine Learning · Statistics 2026-05-20 Thiago R. Ramos , Helton Graziadei , Luben M. C. Cabezas
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