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Dynamic Monte Carlo method
About: Dynamic Monte Carlo method is a research topic. Over the lifetime, 13294 publications have been published within this topic receiving 371256 citations.
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TL;DR: In this paper, fast procedures for conducting Monte Carlo experiments involving bootstrap estimators are analyzed and formal results establishing the properties of these methods under general conditions are provided. But they do not provide a detailed analysis of their performance.
Abstract: We analyze fast procedures for conducting Monte Carlo experiments involving bootstrap estimators, providing formal results establishing the properties of these methods under general conditions.
185 citations
185 citations
TL;DR: In this article, a new stochastic method for calculating ground-state properties of quantum systems is introduced, where segments of a Langevin random walk guided by a trial wave function are subject to a Metropolis rejection test performed on the time integral of the local energy.
Abstract: We introduce a new stochastic method for calculating ground-state properties of quantum systems. Segments of a Langevin random walk guided by a trial wave function are subject to a Metropolis rejection test performed on the time integral of the local energy. The algorithm\char22{}which is as simple as variational Monte Carlo\char22{}for bosons provides exact expectation values of local observables, as well as their static and dynamic (in imaginary time) response functions, without mixed-estimate nor population-control biases. Our method is demonstrated with a few case applications to ${}^{4}\mathrm{He}$.
185 citations
01 Jan 2009
TL;DR: In these notes I discuss Monte Carlo simulations for the study of classical models in statistical mechanics and include a simple and direct proof that the method converges to the Boltzmann distribution.
Abstract: In these notes I discuss Monte Carlo simulations for the study of classical models in statistical mechanics. I include a simple and direct proof that the method converges to the Boltzmann distribution. Usually, physics articles discuss this important point by just giving a reference to the mathematical literature on “Markov chains”, where the proof is rather abstract. In these notes I give a proof of convergence which is self contained and uses only elementary algebra. In statistical mechanics one computes averages of a quantity A from the Boltzmann distribution, i.e. 〈A〉 = ∑
183 citations
TL;DR: It was found that Fickian drug release from cylindrical matrices can be approximated nicely with the Weibull function, and this model has the benefit of providing a simple physical connection between the model parameters and the system geometry, which was something missing from other semiempirical models.
Abstract: Purpose. To verify the Higuchi law and study the drug release from cylindrical and spherical matrices by means of Monte Carlo computer simulation.
183 citations