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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: The theoretical skill of Monte Carlo approximations to the stochastic dynamic forecasting technique proposed by Epstein is examined by means of an extension of earlier atmospheric predictability studies that used the test-field model of two-dimensional turbulence as mentioned in this paper.
Abstract: The theoretical skill of Monte Carlo approximations to the stochastic dynamic forecasting technique proposed by Epstein is examined by means of an extension of earlier atmospheric predictability studies that used the test-field model of two-dimensional turbulence. The fundamental statistical hydrodynamical concept of an ensemble of phase paths evolving in a dynamical phase space is reviewed and used to define the statistical properties of a finite Monte Carlo sample. The application of a linear regression step to arrive at a final best estimate of the state of the atmosphere is also discussed. The resulting forecasts approach the climatological mean at forecast times so late that all skill has been lost. For an ideal case with an observing resolution, hopefully achievable in the 1980s with satellite-based sensors, it is found that the. Monte Carlo procedure leads to the greatest improvement in mean-square vector wind forecast skill in the 6- to 10-day range. For another case corresponding roughly...
755 citations
TL;DR: In this article, a series of Monte Carlo simulations has been carried out to characterize the temperature and size dependence of the results for liquid water using the TIP4P potential function.
Abstract: A series of Monte Carlo simulations has been carried out to characterize the temperature and size dependence of the results for liquid water using the TIP4P potential function. Five temperatures from -25 to 100°C and four system sizes from 64 to 512 molecules have been studied. Comparisons are made with experimental thermodynamic and structural data as well as results of prior simulations.
728 citations
TL;DR: A powerful and flexible MCMC algorithm for stochastic simulation that builds on a pseudo-marginal method, showing how algorithms which are approximations to an idealized marginal algorithm, can share the same marginal stationary distribution as the idealized method.
Abstract: We introduce a powerful and flexible MCMC algorithm for stochastic simulation. The method builds on a pseudo-marginal method originally introduced in [Genetics 164 (2003) 1139--1160], showing how algorithms which are approximations to an idealized marginal algorithm, can share the same marginal stationary distribution as the idealized method. Theoretical results are given describing the convergence properties of the proposed method, and simple numerical examples are given to illustrate the promising empirical characteristics of the technique. Interesting comparisons with a more obvious, but inexact, Monte Carlo approximation to the marginal algorithm, are also given.
723 citations
723 citations
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TL;DR: The Hybrid Monte Carlo (HMC) algorithm for lattice gauge theory calculations as discussed by the authors is a large step method which has none of the discrete step size errors usually associated with the Molecular Dynamics, Langevin, or Hybrid algorithms.
Abstract: I discuss the Hybrid Monte Carlo algorithm for performing lattice gauge theory calculations. This is a large step method which has none of the discrete step size errors usually associated with the Molecular Dynamics, Langevin, or Hybrid algorithms. The method allows the inclusion of dynamical fermion fields in a straightforward way.
722 citations