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Related papers: Inference in Ising Models

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Maximum pseudolikelihood method has been among the most important methods for learning parameters of statistical physics models, such as Ising models. In this paper, we study how pseudolikelihood can be derived for learning parameters of a…

Machine Learning · Computer Science 2015-06-09 Onur Dikmen

Maximum Likelihood Estimators (MLE) has many good properties. For example, the asymptotic variance of MLE solution attains equality of the asymptotic Cram{\'e}r-Rao lower bound (efficiency bound), which is the minimum possible variance for…

Machine Learning · Statistics 2019-11-05 Song Liu , Takafumi Kanamori , Wittawat Jitkrittum , Yu Chen

We study the asymptotic properties of parameter estimation and predictive inference under the exchangeable Gibbs partition, characterized by a discount parameter $\alpha\in(0,1)$ and a triangular array $v_{n,k}$ satisfying a backward…

Statistics Theory · Mathematics 2026-04-07 Takuya Koriyama

Ising spin glasses with bimodal and Gaussian near-neighbor interaction distributions are studied through numerical simulations. The non-self-averaging (normalized inter-sample variance) parameter $U_{22}(T,L)$ for the spin glass…

Disordered Systems and Neural Networks · Physics 2016-01-20 P. H. Lundow , I. A. Campbell

Consider a setting with $N$ independent individuals, each with an unknown parameter, $p_i \in [0, 1]$ drawn from some unknown distribution $P^\star$. After observing the outcomes of $t$ independent Bernoulli trials, i.e., $X_i \sim…

Statistics Theory · Mathematics 2019-02-13 Ramya Korlakai Vinayak , Weihao Kong , Gregory Valiant , Sham M. Kakade

This paper reports numerical studies of a compressible version of the Ising spin glass in two dimensions. Compressibility is introduced by adding a term that couples the spin-spin interactions and local lattice deformations to the standard…

Disordered Systems and Neural Networks · Physics 2013-05-29 Adam H. Marshall

We calculate high-temperature graph expansions for the Ising spin glass model with 4 symmetric random distribution functions for its nearest neighbor interaction constants J_{ij}. Series for the Edwards-Anderson susceptibility \chi_EA are…

Disordered Systems and Neural Networks · Physics 2009-11-10 Daniel Daboul , Iksoo Chang , Amnon Aharony

In a seminal paper (Weitz, 2006), Weitz gave a deterministic fully polynomial approximation scheme for count- ing exponentially weighted independent sets (equivalently, approximating the partition function of the hard-core model from…

Discrete Mathematics · Computer Science 2015-03-19 Alistair Sinclair , Piyush Srivastava , Marc Thurley

We study ferromagnetic Ising models on finite graphs with an inhomogeneous external field, where a subset of vertices is designated as the boundary. We show that the influence of boundary conditions on any given spin is maximised when the…

Probability · Mathematics 2022-03-29 Jian Ding , Jian Song , Rongfeng Sun

We study the identity testing problem in the context of spin systems or undirected graphical models, where it takes the following form: given the parameter specification of the model $M$ and a sampling oracle for the distribution…

Data Structures and Algorithms · Computer Science 2019-06-21 Ivona Bezakova , Antonio Blanca , Zongchen Chen , Daniel Štefankovič , Eric Vigoda

In a recent paper we found strong evidence from simulations that the Isingantiferromagnet on ``thin'' random graphs - Feynman diagrams - displayed amean-field spin glass transition. The intrinsic interest of considering such random graphs…

High Energy Physics - Lattice · Physics 2016-09-01 C. F. Baillie , W. Janke , D. A. Johnston , P. Plechac

The Ising antiferromagnet is an important statistical physics model with close connections to the {\sc Max Cut} problem. Combining spatial mixing arguments with the method of moments and the interpolation method, we pinpoint the replica…

Combinatorics · Mathematics 2020-11-13 Amin Coja-Oghlan , Philipp Loick , Balázs F. Mezei , Gregory B. Sorkin

We consider estimation and inference in a single index regression model with an unknown but smooth link function. In contrast to the standard approach of using kernels or regression splines, we use smoothing splines to estimate the smooth…

Methodology · Statistics 2019-05-28 Arun Kumar Kuchibhotla , Rohit Kumar Patra

Introduced by Kiefer and Wolfowitz \cite{KW56}, the nonparametric maximum likelihood estimator (NPMLE) is a widely used methodology for learning mixture odels and empirical Bayes estimation. Sidestepping the non-convexity in mixture…

Statistics Theory · Mathematics 2020-09-08 Yury Polyanskiy , Yihong Wu

We present a detailed study of the phase diagram of the Ising model in random graphs with arbitrary degree distribution. By using the replica method we compute exactly the value of the critical temperature and the associated critical…

Statistical Mechanics · Physics 2009-11-07 M. Leone , A. Vazquez , A. Vespignani , R. Zecchina

We investigate thermodynamic phase transitions of the joint presence of spin glass (SG) and random field (RF) using a random graph model that allows us to deal with the quenched disorder. Therefore, the connectivity becomes a controllable…

Disordered Systems and Neural Networks · Physics 2021-02-24 R. Erichsen , A. Silveira , S. G. Magalhaes

Recently maximum pseudo-likelihood (MPL) inference method has been successfully applied to statistical physics models with intractable likelihoods. We use information theory to derive a relation between the pseudo-likelihood and likelihood…

Disordered Systems and Neural Networks · Physics 2015-06-18 Alexander Mozeika , Onur Dikmen , Joonas Piili

We introduce an Ising spin-glass model with correlated disorder which continuously interpolates between the pure ferromagnetic Ising model and the Edwards-Anderson model with symmetric disorder. For this model, we prove that a Nishimori…

Disordered Systems and Neural Networks · Physics 2026-05-19 Hidetoshi Nishimori

Among the Renormalization Group Theory scaling rules relating critical exponents, there are hyperscaling rules involving the dimension of the system. It is well known that in Ising models hyperscaling breaks down above the upper critical…

Disordered Systems and Neural Networks · Physics 2018-01-24 P. H. Lundow , I. A. Campbell

It is widely accepted that the free energy F(H) of the two-dimensional Ising model in the ferromagnetic phase T<T_c has an essential branch cut singularity at the origin H=0. The phenomenological droplet theory predicts that near the cut…

Condensed Matter · Physics 2007-05-23 S. B. Rutkevich
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