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We develop a recently proposed importance-sampling Monte Carlo algorithm for sampling rare events and quenched variables in random disordered systems. We apply it to a two dimensional bond-diluted Ising model and study the Griffiths…

Disordered Systems and Neural Networks · Physics 2009-11-13 Koji Hukushima , Yukito Iba

Correlations may affect propagation processes on complex networks. To analyze their effect, it is useful to build ensembles of networks constrained to have a given value of a structural measure, such as the degree-degree correlation $r$,…

Statistical Mechanics · Physics 2013-04-09 Marlon Ramos , Celia Anteneodo

We consider a random object that is associated with both random walks and random media, specifically, the superposition of a configuration of subcritical Bernoulli percolation on an infinite connected graph and the trace of the simple…

Probability · Mathematics 2019-09-10 Kazuki Okamura

We consider Monte Carlo approximations to the maximum likelihood estimator in models with intractable norming constants. This paper deals with adaptive Monte Carlo algorithms, which adjust control parameters in the course of simulation. We…

Methodology · Statistics 2016-12-08 Blazej Miasojedow , Wojciech Niemiro , Jan Palczewski , Wojciech Rejchel

We study the thermodynamic behavior of the random chain model proposed by Iori, Marinari and Parisi, and how this depends on the actual sequence of interactions along the chain. The properties of randomly chosen sequences are compared to…

chem-ph · Physics 2016-08-31 Anders Irback , Holm Schwarze

Two approaches to studying the correlation functions of the binary Markov sequences are considered. The first of them is based on the study of probability of occurring different ''words'' in the sequence. The other one uses recurrence…

Data Analysis, Statistics and Probability · Physics 2007-05-23 S. S. Apostolov , Z. A. Mayzelis , O. V. Usatenko , V. A. Yampol'skii

We explain in detail how to estimate mean values and assess statistical errors for arbitrary functions of elementary observables in Monte Carlo simulations. The method is to estimate and sum the relevant autocorrelation functions, which is…

High Energy Physics - Lattice · Physics 2009-09-29 Ulli Wolff

Random numbers play a crucial role in science and industry. Many numerical methods require the use of random numbers, in particular the Monte Carlo method. Therefore it is of paramount importance to have efficient random number generators.…

Computational Physics · Physics 2010-05-25 Helmut G. Katzgraber

Machines whose main purpose is to permute and sort data are studied. The sets of permutations that can arise are analysed by means of finite automata and avoided pattern techniques. Conditions are given for these sets being enumerated by…

Combinatorics · Mathematics 2007-05-23 M. Albert , M. D. Atkinson , N. Ruskuc

Revisions are almost entirely in the introduction and conclusion. Results are unchanged, however the comments and recommendations on different generators were changed, and more references were added.

Condensed Matter · Physics 2009-10-22 P. D. Coddington

Multiscale correlation functions in high Reynolds number experimental turbulence and synthetic signals are investigated. Fusion Rules predictions as they arise from multiplicative, almost uncorrelated, random processes for the energy…

chao-dyn · Physics 2009-10-31 R. Benzi , L. Biferale , F. Toschi

A general scheme for devising efficient cluster dynamics proposed in a previous letter [Phys.Rev.Lett. 72, 1541 (1994)] is extensively discussed. In particular the strong connection among equilibrium properties of clusters and dynamic…

Condensed Matter · Physics 2009-10-28 V. Cataudella , G. Franzese , M. Nicodemi , A. Scala , A. Coniglio

The class of random-cluster models is a unification of a variety of stochastic processes of significance for probability and statistical physics, including percolation, Ising, and Potts models; in addition, their study has impact on the…

Probability · Mathematics 2007-05-23 Geoffrey Grimmett

Analytical results are derived for the bond percolation threshold and the size of the giant connected component in a class of random networks with non-zero clustering. The network's degree distribution and clustering spectrum may be…

Statistical Mechanics · Physics 2009-09-22 James P. Gleeson

The machine learning community has recently devoted much attention to the problem of inferring causal relationships from statistical data. Most of this work has focused on uncovering connections among scalar random variables. We generalize…

Machine Learning · Statistics 2012-07-10 Doris Entner , Patrik O. Hoyer

This article is devoted to methods of construction and study of stochastic models based on Monte Carlo method. A model of Brownian motion, the construction and processing which brings to a world of random numbers and mathematical…

Physics Education · Physics 2018-09-18 Illia O. Teplytskyi , Serhiy O. Semerikov

We investigate the prevalence of sample repetition in a Sequential Monte Carlo (SMC) method recently introduced for political redistricting.

Probability · Mathematics 2024-10-01 Sarah Cannon , Daryl DeFord , Moon Duchin

A statistical inference method is developed and tested for pairwise interacting systems whose degrees of freedom are continuous angular variables, such as planar spins in magnetic systems or wave phases in optics and acoustics. We…

Statistical Mechanics · Physics 2015-06-15 P. Tyagi , A. Pagnani , F. Antenucci , M. Ibáñez Berganza , L. Leuzzi

In this expository article, we highlight the direct connection between card shuffling and the functions known as $P$-partitions that come from algebraic combinatorics. While many (but not all) of the results we discuss are known, we give a…

Combinatorics · Mathematics 2021-04-28 Jason Fulman , T. Kyle Petersen

Correlations are known to play a crucial role in determining the structure of complex networks. Here we study how their presence affects the computation of the percolation threshold in random hypergraphs. In order to mimic the correlation…

Disordered Systems and Neural Networks · Physics 2009-07-20 Serena Bradde , Ginestra Bianconi
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