Stochastic Bouncy Particle Sampler


Ari Pakman, Dar Gilboa, David Carlson, Liam Paninski ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:2741-2750, 2017.


We introduce a stochastic version of the non-reversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear, to efficiently sample Bayesian posteriors in big datasets. We prove that in the BPS no bias is introduced by noisy evaluations of the log-likelihood gradient. On the other hand, we argue that efficiency considerations favor a small, controllable bias, in exchange for faster mixing. We introduce a simple method that controls this trade-off. We illustrate these ideas in several examples which outperform previous approaches.

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