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Related papers: Avoiding Boundary Effects in Wang-Landau Sampling

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This paper explores the possibilities and limitations of error correction by the structural simplicity of error mechanisms. Specifically, we consider channel models, called \emph{samplable additive channels}, in which (a) errors are…

Information Theory · Computer Science 2018-05-15 Kenji Yasunaga

A sufficient condition for the uniqueness of multinomial sequential unbiased estimators is provided generalizing a classical result for binomial samples. Unbiased estimators are applied to infer the parameters of multidimensional or…

Statistics Theory · Mathematics 2014-03-06 Enrico Bibbona , Alessandro Rubba

Performing random walks in networks is a fundamental primitive that has found numerous applications in communication networks such as token management, load balancing, network topology discovery and construction, search, and peer-to-peer…

Distributed, Parallel, and Cluster Computing · Computer Science 2012-01-12 Atish Das Sarma , Anisur Rahaman Molla , Gopal Pandurangan

In this paper, we consider the problem of column subset selection. We present a novel analysis of the spectral norm reconstruction for a simple randomized algorithm and establish a new bound that depends explicitly on the sampling…

Numerical Analysis · Mathematics 2015-05-05 Tianbao Yang , Lijun Zhang , Rong Jin , Shenghuo Zhu

We establish via a probabilistic approach the quenched invariance principle for a class of long range random walks in independent (but not necessarily identically distributed) balanced random environments, with the transition probability…

Probability · Mathematics 2020-10-27 Xin Chen , Zhen-Qing Chen , Takashi Kumagai , Jian Wang

We propose a family of lagged random walk sampling methods in simple undirected graphs, where transition to the next state (i.e. node) depends on both the current and previous states -- hence, lagged. The existing random walk sampling…

Statistics Theory · Mathematics 2022-05-16 Li-Chun Zhang

We study the performance of Monte Carlo simulations that sample a broad histogram in energy by determining the mean first-passage time to span the entire energy space of d-dimensional ferromagnetic Ising/Potts models. We first show that…

Statistical Mechanics · Physics 2007-05-23 Yong Wu , Mathias Koerner , Louis Colonna-Romano , Simon Trebst , Harvey Gould , Jonathan Machta , Matthias Troyer

We introduce a parallel Wang-Landau method based on the replica-exchange framework for Monte Carlo simulations. To demonstrate its advantages and general applicability for simulations of complex systems, we apply it to different spin models…

Computational Physics · Physics 2013-05-27 Thomas Vogel , Ying Wai Li , Thomas Wüst , David P. Landau

In this article, we study linearly edge-reinforced random walk on general multi-level ladders for large initial edge weights. For infinite ladders, we show that the process can be represented as a random walk in a random environment, given…

Probability · Mathematics 2007-05-23 Franz Merkl , Silke W. W. Rolles

A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science.…

Social and Information Networks · Computer Science 2020-08-11 Feng Xia , Jiaying Liu , Hansong Nie , Yonghao Fu , Liangtian Wan , Xiangjie Kong

For algorithms based on interacting particle systems that admit a mean-field description, convergence analysis is often more accessible at the mean-field level. In order to transfer convergence results obtained at the mean-field level to…

Probability · Mathematics 2025-11-03 Nicolai Jurek Gerber , Franca Hoffmann , Urbain Vaes

The best practical techniques for exact solution of instances of the constrained maximum-entropy sampling problem, a discrete-optimization problem arising in the design of experiments, are via a branch-and-bound framework, working with a…

Optimization and Control · Mathematics 2024-02-19 Zhongzhu Chen , Marcia Fampa , Jon Lee

The paper studies empirically the time-space trade-off between sampling and inference in a sl cutset sampling algorithm. The algorithm samples over a subset of nodes in a Bayesian network and applies exact inference over the rest.…

Artificial Intelligence · Computer Science 2012-12-12 Bozhena Bidyuk , Rina Dechter

This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error on each training point of a biased sample to…

Machine Learning · Computer Science 2008-12-18 Corinna Cortes , Mehryar Mohri , Michael Riley , Afshin Rostamizadeh

Graph-limit theory focuses on the convergence of sequences of graphs when the number of nodes becomes arbitrarily large. This framework defines a continuous version of graphs allowing for the study of dynamical systems on very large graphs,…

Probability · Mathematics 2020-05-20 Julien Petit , Renaud Lambiotte , Timoteo Carletti

In this paper we investigate error-thresholds on dynamics fitness-landscapes. We show that there exists both lower and an upper threshold, representing limits to the copying fidelity of simple replicators. The lower bound can be expressed…

Biological Physics · Physics 2009-10-31 Martin Nilsson , Nigel Snoad

We consider a symmetric random walk on the $\nu$-dimensional lattice, whose exit probability from the origin is modified by an antisymmetric perturbation and prove the local central limit theorem for this process. A short-range correction…

Probability · Mathematics 2019-08-09 Giuseppe Genovese , Renato Lucà

Sampling theory concerns the problem of reconstruction of functions from the knowledge of their values at some discrete set of points. In this paper we derive an orthogonal sampling theory and associated Lagrange interpolation formulae from…

Classical Analysis and ODEs · Mathematics 2015-06-26 Luis O. Silva , Julio H. Toloza

Spectral-spatial processing has been increasingly explored in remote sensing hyperspectral image classification. While extensive studies have focused on developing methods to improve the classification accuracy, experimental setting and…

Computer Vision and Pattern Recognition · Computer Science 2017-03-08 Jie Liang , Jun Zhou , Yuntao Qian , Lian Wen , Xiao Bai , Yongsheng Gao

Statistical model checking avoids the exponential growth of states associated with probabilistic model checking by estimating properties from multiple executions of a system and by giving results within confidence bounds. Rare properties…

Performance · Computer Science 2012-01-26 Cyrille Jégourel , Axel Legay , Sean Sedwards