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Related papers: Simulations for Karlin random fields

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An aggregated model is proposed, of which the partial-sum process scales to the Karlin stable processes recently investigated in the literature. The limit extremes of the proposed model, when having regularly-varying tails, are…

Probability · Mathematics 2021-07-09 Yi Shen , Yizao Wang , Na Zhang

Dynamical scaling is an asymptotic property typical for the dynamics of first-order phase transitions in physical systems and related to self-similarity. Based on the integral-representation for the marginal probabilities of a fractional…

Probability · Mathematics 2021-07-23 Markus Kreer

The simulation of spin models close to critical points of continuous phase transitions is heavily impeded by the occurrence of critical slowing down. A number of cluster algorithms, usually based on the Fortuin-Kasteleyn representation of…

Statistical Mechanics · Physics 2013-10-04 Eren Metin Elçi , Martin Weigel

The pseudo likelihood method of Besag(1974), has remained a popular method for estimating Markov random field on a very large lattice, despite various documented deficiencies. This is partly because it remains the only computationally…

Methodology · Statistics 2016-01-12 Wanchuang Zhu , Yanan Fan

Max-stable processes have proved to be useful for the statistical modelling of spatial extremes. Several representations of max-stable random fields have been proposed in the literature. One such representation is based on a limit of…

Methodology · Statistics 2012-04-26 Richard A. Davis , Claudia Klüppelberg , Christina Steinkohl

Generalized Brown-Resnick processes form a flexible class of stationary max-stable processes based on Gaussian random fields. With regard to applications fast and accurate simulation of these processes is an important issue. In fact,…

Probability · Mathematics 2010-09-30 Marco Oesting

We provide a new kriging procedure of processes on graphs. Based on the construction of Gaussian random processes indexed by graphs, we extend to this framework the usual linear prediction method for spatial random fields, known as kriging.…

Statistics Theory · Mathematics 2014-06-26 Thibault Espinasse , Jean-Michel Loubes

This paper is devoted to the study of simulating a large class of self-similar processes. Since most current simulation approaches are limited to case-by-case studies, every existing approach has its constraints and flaws; hence a general…

Probability · Mathematics 2025-12-09 Qidi Peng , William Wu

The article is devoted to the resampling approach application to the reliability problems. This approach to reliability problems was first proposed by Ivnitsky (1967). Resampling is intensive statistical computer method, which is…

Applications · Statistics 2013-04-25 Maxim Fioshin , Helen Fioshina

A new and very general technique for simulating solid-fluid suspensions has been described in a previous paper (Part I); the most important feature of the new method is that the computational cost scales with the number of particles. In…

comp-gas · Physics 2009-10-22 Anthony J. C. Ladd

Atypical, rare trajectories of dynamical systems are important: they are often the paths for chemical reactions, the haven of (relative) stability of planetary systems, the rogue waves that are detected in oil platforms, the structures that…

Statistical Mechanics · Physics 2012-04-12 Cristian Giardina , Jorge Kurchan , Vivien Lecomte , Julien Tailleur

We present a new method for simulating Markovian jump processes with time-dependent transitions rates, which avoids the transformation of random numbers by inverting time integrals over the rates. It relies on constructing a sequence of…

Statistical Mechanics · Physics 2015-05-20 Viktor Holubec , Petr Chvosta , Mario Einax , Philipp Maass

Recently the regular conditional distributions of max-infinitely divisible processes were derived by \citet{Dombry2011} and although these conditional distributions have complicated closed forms, \citet{Dombry2011b} introduce an algorithm…

Statistics Theory · Mathematics 2012-08-28 Clément Dombry , Mathieu Ribatet

Max-stable processes are widely used to model spatial extremes. These processes exhibit asymptotic dependence meaning that the large values of the process can occur simultaneously over space. Recently, inverted max-stable processes have…

Probability · Mathematics 2015-01-20 Ioannis Papastathopoulos , Jonathan A. Tawn

We investigate a special case of infinite urn schemes first considered by Karlin (1967), especially its occupancy and odd-occupancy processes. We first propose a natural randomization of these two processes and their decompositions. We then…

Probability · Mathematics 2015-08-07 Olivier Durieu , Yizao Wang

Nested sampling is a simulation method for approximating marginal likelihoods proposed by Skilling (2006). We establish that nested sampling has an approximation error that vanishes at the standard Monte Carlo rate and that this error is…

Computation · Statistics 2010-10-11 Nicolas Chopin , Christian Robert

The conventional spin dynamics simulations are performed in direct products of state spaces of individual spins. In a general system of n spins, the total number of elements in the state basis is >4^n. A system propagation step requires an…

Computational Physics · Physics 2014-07-16 Ilya Kuprov , Nicola Wagner-Rundell , P. J. Hore

We propose and analyse new stabilized time marching schemes for Phase Fields model such as Allen-Cahn and Cahn-Hillard equations, when discretized in space with high order finite differences compact schemes. The stabilization applies to…

Numerical Analysis · Mathematics 2019-10-01 Matthieu Brachet , Jean-Paul Chehab

The estimation of small probabilities of failure from computer simulations is a classical problem in engineering, and the Subset Simulation algorithm proposed by Au & Beck (Prob. Eng. Mech., 2001) has become one of the most popular method…

Computation · Statistics 2012-07-10 Ling Li , Julien Bect , Emmanuel Vazquez

Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of…

Machine Learning · Statistics 2024-10-11 Andrew Zammit-Mangion , Matthew Sainsbury-Dale , Raphaël Huser
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