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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: This work determines the depletion-induced phase-behavior of hard-sphere colloids and interacting polymers by large-scale Monte Carlo simulations using very accurate coarse-graining techniques and shows that including excluded-volume interactions between polymers leads to qualitative differences in the phase diagrams.
Abstract: We determine the depletion-induced phase-behavior of hard-sphere colloids and interacting polymers by large-scale Monte Carlo simulations using very accurate coarse-graining techniques. A comparison with standard Asakura-Oosawa model theories and simulations shows that including excluded-volume interactions between polymers leads to qualitative differences in the phase diagrams. These effects become increasingly important for larger relative polymer size. Our simulation results agree quantitatively with recent experiments.
195 citations
TL;DR: The paper presents a technique for efficient Monte Carlo type simulation of samples of random vectors with prescribed marginals and a correlation structure, and it is shown that if the technique is applied for small-sample simulation with a variance reduction technique called Latin Hypercube Sampling, the outcome is a set of samples that match user-defined Marginals and covariances.
193 citations
16 Sep 2003
TL;DR: This work proposes to use a Gaussian Process model of the (log of the) posterior for most of the computations required by HMC, allowing Bayesian treatment of models with posteriors that are computationally demanding, such as models involving computer simulation.
Abstract: Hybrid Monte Carlo (HMC) is often the method of choice for computing Bayesian integrals that are not analytically tractable. However the success of this method may require a very large number of evaluations of the (un-normalized) posterior and its partial derivatives. In situations where the posterior is computationally costly to evaluate, this may lead to an unacceptable computational load for HMC. I propose to use a Gaussian Process model of the (log of the) posterior for most of the computations required by HMC. Within this scheme only occasional evaluation of the actual posterior is required to guarantee that the samples generated have exactly the desired distribution, even if the GP model is somewhat inaccurate. The method is demonstrated on a 10 dimensional problem, where 200 evaluations suffice for the generation of 100 roughly independent points from the posterior. Thus, the proposed scheme allows Bayesian treatment of models with posteriors that are computationally demanding, such as models involving computer simulation.
193 citations
TL;DR: This work applies the maximum-entropy method to the analytic continuation of quantum Monte Carlo data to obtain real-frequency spectral functions and reports encouraging preliminary results for the Fano-Anderson model of an impurity state in a continuum.
Abstract: An outstanding problem in the simulation of condensed-matter phenomena is how to obtain dynamical information. We consider the numerical analytic continuation of imaginary-time quantum Monte Carlo data to obtain real-frequency spectral functions. This is an extremely ill-posed problem similar to the inversion of a Laplace transform. We suggest an image-reconstruction approach, which has been widely applied to data analysis in experimental research. Specifically, we apply the maximum-entropy method (ME) to the analytic continuation of quantum Monte Carlo data. We report encouraging preliminary results for the Fano-Anderson model of an impurity state in a continuum. The incorporation of additional prior information, such as sum rules and asymptotic behavior, can be expected to significantly improve results. We compare (ME) to alternative methods. We also discuss statistical error propagation for the analytic continuation problem via the likelihood function, which is independent of the choice of image-reconstruction method. This includes the sensitivity of the data to structure in the spectral function, the optimization of Monte Carlo simulations, and how to incorporate covariance in the statistical errors of the Monte Carlo method.
193 citations
11 Nov 1998-Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment
TL;DR: In this article, a new simulation of nuclear γ cascades by the Monte Carlo method is described, which makes it possible to generate artificially individual events of the γ-cascade decay of an isolated, highly excited initial level in a medium and heavy nucleus.
Abstract: A new simulation of nuclear γ cascades by the Monte Carlo method is described. It makes it possible to generate artificially individual events of the γ-cascade decay of an isolated, highly excited initial level in a medium and heavy nucleus. A broad class of quantities, associated with the process of γ-cascade de-excitation, can be modelled. The main advantage of the method is the possibility of a full quantitative control over the influence of the Porter–Thomas fluctuations of partial radiation widths on uncertainties of the modelled cascade-related quantities. For assessment of these uncertainties and a control over the accuracy of the method, a special statistical formalism has been developed.
193 citations