Auxiliary-variable exact Hamiltonian Monte Carlo samplers for binary distributions

Ari Pakman; Liam Paninski

Auxiliary-variable exact Hamiltonian Monte Carlo samplers for binary distributions

Ari Pakman, Liam Paninski

Research output: Contribution to journal › Conference article › peer-review

43 Scopus citations

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.

Original language	English
Journal	Advances in Neural Information Processing Systems
State	Published - 1 Jan 2013
Externally published	Yes
Event	27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States Duration: 5 Dec 2013 → 10 Dec 2013

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

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title = "Auxiliary-variable exact Hamiltonian Monte Carlo samplers for binary distributions",

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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language = "English",

journal = "Advances in Neural Information Processing Systems",

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AU - Pakman, Ari

AU - Paninski, Liam

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N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84898958653&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84898958653

SN - 1049-5258

JO - Advances in Neural Information Processing Systems

JF - Advances in Neural Information Processing Systems

T2 - 27th Annual Conference on Neural Information Processing Systems, NIPS 2013

Y2 - 5 December 2013 through 10 December 2013

ER -

Auxiliary-variable exact Hamiltonian Monte Carlo samplers for binary distributions

Abstract

ASJC Scopus subject areas

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