  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.
