Hamiltonian Monte Carlo Without Detailed Balance
Abstract
We present a method for performing Hamiltonian Monte Carlo that largely eliminates sample rejection. In situations that would normally lead to rejection, instead a longer trajectory is computed until a new state is reached that can be accepted. This is achieved using Markov chain transitions that satisfy the fixed point equation, but do not satisfy detailed balance. The resulting algorithm significantly suppresses the random walk behavior and wasted function evaluations that are typically the consequence of update rejection. We demonstrate a greater than factor of two improvement in mixing time on three test problems. We release the source code as Python and MATLAB packages.
Cite
Text
Sohl-Dickstein et al. "Hamiltonian Monte Carlo Without Detailed Balance." International Conference on Machine Learning, 2014.Markdown
[Sohl-Dickstein et al. "Hamiltonian Monte Carlo Without Detailed Balance." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/sohldickstein2014icml-hamiltonian/)BibTeX
@inproceedings{sohldickstein2014icml-hamiltonian,
title = {{Hamiltonian Monte Carlo Without Detailed Balance}},
author = {Sohl-Dickstein, Jascha and Mudigonda, Mayur and DeWeese, Michael},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {719-726},
volume = {32},
url = {https://mlanthology.org/icml/2014/sohldickstein2014icml-hamiltonian/}
}