Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions

2013/11/09 by Ari Pakman, Liam Paninski, Pakman, Ari +1 · 8 citations
Computer Science · Mathematics · Physics and Astronomy · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Physical sciences #Markov Chains and Monte Carlo Methods #Statistical Mechanics (cond-mat.stat-mech) #Stochastic processes and statistical mechanics #cond-mat.stat-mech #stat.CO

paper · pdf · doi:10.48550/arxiv.1311.2166

11 pages, 4 figures. Proceedings of the 27th Annual Conference Neural Information Processing Systems (NIPS), 2013

openalex publication_date 2013/11/09 · arxiv created 2015/10/12 · arxiv updated 2015/10/13 · openalex created_date 2025/10/27 · openalex updated_date 2026/07/28

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.

Citations

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