HMC: avoiding rejections by not using leapfrog and some results on the acceptance rate

12/06/2019
by   M. P. Calvo, et al.
0

We give numerical evidence that the standard leapfrog algorithm may not be the best integrator to use within the Hamiltonian Monte Carlo (HMC) method and its variants. When the dimensionality of the target distribution is high, the number of accepted proposals obtained with a given computational effort may be multiplied by a factor of three or more by switching to alternative integrators very similar to leapfrog. Such integrators have appeared in the numerical analysis and molecular dynamics literature rather than in the statistics literature. In addition, we provide some theoretical results on the acceptance rate of HMC.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/06/2019

HMC: avoiding rejections by not using leapfrog and an analysis of the acceptance rate

We give numerical evidence that the standard leapfrog algorithm may not ...
research
11/14/2017

Geometric integrators and the Hamiltonian Monte Carlo method

This paper surveys in detail the relations between numerical integration...
research
12/12/2022

Acceptance Rates of Invertible Neural Networks on Electron Spectra from Near-Critical Laser-Plasmas: A Comparison

While the interaction of ultra-intense ultra-short laser pulses with nea...
research
03/28/2023

Unbiasing Hamiltonian Monte Carlo algorithms for a general Hamiltonian function

Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo method that ...
research
06/14/2022

Conservative Hamiltonian Monte Carlo

We introduce a new class of Hamiltonian Monte Carlo (HMC) algorithm call...
research
02/25/2016

Towards Unifying Hamiltonian Monte Carlo and Slice Sampling

We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demo...
research
02/21/2020

A micro-macro Markov chain Monte Carlo method for molecular dynamics using reaction coordinate proposals I: direct reconstruction

We introduce a new micro-macro Markov chain Monte Carlo method (mM-MCMC)...

Please sign up or login with your details

Forgot password? Click here to reset