Binary Bouncy Particle Sampler
Computation
2017-11-06 v1 Machine Learning
Abstract
The Bouncy Particle Sampler is a novel rejection-free non-reversible sampler for differentiable probability distributions over continuous variables. We generalize the algorithm to piecewise differentiable distributions and apply it to generic binary distributions using a piecewise differentiable augmentation. We illustrate the new algorithm in a binary Markov Random Field example, and compare it to binary Hamiltonian Monte Carlo. Our results suggest that binary BPS samplers are better for easy to mix distributions.
Cite
@article{arxiv.1711.00922,
title = {Binary Bouncy Particle Sampler},
author = {Ari Pakman},
journal= {arXiv preprint arXiv:1711.00922},
year = {2017}
}
Comments
4 pages