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Journal ArticleDOI

Higher-order hybrid Monte Carlo algorithms

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TLDR
A simple recursive iteration of the leapfrog discretization of Newton's equations leads to a removal of the finite-step-size error to any desired order in a manner that preserves phase-space areas and reversibility.
Abstract
We present a simple recursive iteration of the leapfrog discretization of Newton's equations which leads to a removal of the finite-step-size error to any desired order. This is done in a manner that preserves phase-space areas and reversibility, as required for use in the hybrid Monte Carlo method for simulating fermionic fields. The resulting asymptotic volume dependence is exp((ln/ital V/)/sup 1/2/). We test the scheme on the (2+1)-dimensional Hubbard model.

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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.
Journal ArticleDOI

Reversible multiple time scale molecular dynamics

TL;DR: It is shown how the new RESPA methods are related to predictor–corrector integrators and how these methods can be used to accelerate the integration of the equations of motion of systems with Nose thermostats.
BookDOI

MCMC using Hamiltonian dynamics

Radford M. Neal
- 09 Jun 2012 - 
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

Explicit reversible integrators for extended systems dynamics

TL;DR: Explicit reversible integrators, suitable for use in large-scale computer simulations, are derived for extended systems generating the canonical and isothermal-isobaric ensembles.
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.
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