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arXiv · 1311.2166

Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions

Ari Pakman·Liam Paninski
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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.

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Ari Pakman, Liam Paninski. 2013-11-09. Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions. https://arxiv.org/abs/1311.2166

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