Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions

NeurIPS 2013  ·  Pakman Ari, Paninski Liam ·

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. read more

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Computation Statistical Mechanics

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