Journal ArticleDOI
Brownian dynamics as smart Monte Carlo simulation
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In this paper, a new Monte Carlo simulation procedure is developed which is expected to produce more rapid convergence than the standard Metropolis method, and the trial particle moves are chosen in accord with a Brownian dynamics algorithm rather than at random.Abstract:
A new Monte Carlo simulation procedure is developed which is expected to produce more rapid convergence than the standard Metropolis method. The trial particle moves are chosen in accord with a Brownian dynamics algorithm rather than at random. For two model systems, a string of point masses joined by harmonic springs and a cluster of charged soft spheres, the new procedure is compared to the standard one and shown to manifest a more rapid convergence rate for some important energetic and structural properties.read more
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Book
Bayesian learning for neural networks
TL;DR: Bayesian Learning for Neural Networks shows that Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional neural network learning methods.
BookDOI
MCMC using Hamiltonian dynamics
TL;DR: In this paper, the authors discuss theoretical and practical aspects of Hamiltonian Monte Carlo, and present some of its variations, including using windows of states for deciding on acceptance or rejection, computing trajectories using fast approximations, tempering during the course of a trajectory to handle isolated modes, and short-cut methods that prevent useless trajectories from taking much computation time.
Journal ArticleDOI
Dynamic strength of molecular adhesion bonds.
Evan Evans,Ken Ritchie +1 more
TL;DR: How Brownian dynamics can help bridge the gap between molecular dynamics and probe tests is described, which shows that bond strength progresses through three dynamic regimes of loading rate.
Journal ArticleDOI
Path integrals in the theory of condensed helium
TL;DR: In this paper, the authors introduce a picture of a boson superfluid and show how superfluidity and Bose condensation manifest themselves, showing the excellent agreement between simulations and experimental measurements on liquid and solid helium for such quantities as pair correlations, the superfluid density, the energy, and the momentum distribution.
Book ChapterDOI
MCMC Using Hamiltonian Dynamics
TL;DR: This volume focuses on perfect sampling or exact sampling algorithms, so named because such algorithms use Markov chains and yet obtain genuine i.i.d. draws—hence perfect or exact—from their limiting distributions within a finite numbers of iterations.
References
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Journal ArticleDOI
Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
Journal ArticleDOI
Monte Carlo Sampling Methods Using Markov Chains and Their Applications
TL;DR: A generalization of the sampling method introduced by Metropolis et al. as mentioned in this paper is presented along with an exposition of the relevant theory, techniques of application and methods and difficulties of assessing the error in Monte Carlo estimates.
Book
Analysis of Variance
TL;DR: The authors have improved on their widely used first edition by adding material on how to do ANOVA using statistical packages for microcomputers, linking the use of ANOVA to regression analysis, and enchancing their discussion on using ANOVA for experimentally gathered data.
Journal ArticleDOI
Monte Carlo simulation of a many-fermion study
TL;DR: In this paper, the Metropolis Monte Carlo method is used to sample the square of an antisymmetric wave function composed of a product of a Jastrow wave function and a number of Slater determinants.