Binary Bouncy Particle Sampler

2017/11/02 by Ari Pakman, Pakman, Ari · 1 citation
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Statistical Methods and Bayesian Inference #stat.CO #stat.ML

paper · pdf · doi:10.48550/arxiv.1711.00922

4 pages

arxiv created 2017/11/02 · openalex publication_date 2017/11/02 · arxiv updated 2017/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The Bouncy Particle Sampler is a novel rejection-free non-reversible sampler for differentiable probability distributions over continuous variables. We generalize the algorithm to piecewise differentiable distributions and apply it to generic binary distributions using a piecewise differentiable augmentation. We illustrate the new algorithm in a binary Markov Random Field example, and compare it to binary Hamiltonian Monte Carlo. Our results suggest that binary BPS samplers are better for easy to mix distributions.

Citations

• Exact Hamiltonian Monte Carlo for Truncated Multivariate Gaussians
• Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions
• Piecewise-Deterministic Markov Chain Monte Carlo

Cited by

• Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler

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• Bouncy Hybrid Sampler as a Unifying Device
• Stochastic Bouncy Particle Sampler
• A Discrete Bouncy Particle Sampler
• The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method