2013
NeurIPS
NeurIPS 2013
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 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.
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Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
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Hot Topic Early Bird
- posterior sampling
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Optimization & Theory > Stochastic Processes
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Statistics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Inference
Mathematics & Optimization > Probability > Stochastic Processes
Machine Learning > Bayesian & Probabilistic > Markov Chain Monte Carlo