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Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions
Ari Pakman · Liam Paninski

Fri Dec 06 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor

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 continuous 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.

Author Information

Ari Pakman (Columbia University)
Liam Paninski (Columbia University)

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