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2013 NIPS 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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🧭 Keyword Pioneer — binary distributions
🐣 Hot Topic Early Bird — markov chain monte carlo
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
📈 Trend Setter — Markov Chain Monte Carlo

Authors

Ari Pakman , Liam Paninski

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Optimization & Theory > Stochastic Processes Machine Learning > Optimization & Theory > Statistics Machine Learning > Bayesian & Probabilistic > Bayesian Inference Mathematics & Optimization > Probability > Stochastic Processes Machine Learning > Bayesian & Probabilistic > Markov Chain Monte Carlo

Keywords

bayesian inference posterior sampling markov chain monte carlo bayesian sampling spike-and-slab prior posterior inference mcmc sampling auxiliary variable hamiltonian monte carlo markov chain sampling binary distributions bayesian posterior sampling auxiliary variable methods
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