Binary Bouncy Particle Sampler

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.

4 pages

References in corpus (6)

• Rejection-free Monte-Carlo sampling for general potentials
• Piecewise-Deterministic Markov Chain Monte Carlo
• Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains
• Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions
• Piecewise deterministic simulated annealing
• Stochastic Bouncy Particle Sampler

Cited by in corpus (4)

• Ergodicity of the zigzag process
• Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler
• Non-reversible, tuning- and rejection-free Markov chain Monte Carlo via iterated random functions
• Bouncy Hybrid Sampler as a Unifying Device