Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions
Computation
2015-10-13 v2 Statistical Mechanics
Abstract
We present a new approach to sample from generic binary distributions, based on an exact Hamiltonian Monte Carlo algorithm applied to a piecewise continuous augmentation of the binary distribution of interest. An extension of this idea to distributions over mixtures of binary and possibly-truncated Gaussian or exponential variables allows us to sample from posteriors of linear and probit regression models with spike-and-slab priors and truncated parameters. We illustrate the advantages of these algorithms in several examples in which they outperform the Metropolis or Gibbs samplers.
Cite
@article{arxiv.1311.2166,
title = {Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions},
author = {Ari Pakman and Liam Paninski},
journal= {arXiv preprint arXiv:1311.2166},
year = {2015}
}
Comments
11 pages, 4 figures. Proceedings of the 27th Annual Conference Neural Information Processing Systems (NIPS), 2013