Stochastic bouncy particle sampler

Ari Pakman, Dar Gilboa, David Carlson, Liam Paninski

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

We introduce a stochastic version of the nonreversible, 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 tradc-off. We illustrate these ideas in several examples which outperform previous approaches.

Original languageEnglish
Title of host publication34th International Conference on Machine Learning, ICML 2017
PublisherInternational Machine Learning Society (IMLS)
Pages4186-4208
Number of pages23
ISBN (Electronic)9781510855144
StatePublished - 1 Jan 2017
Externally publishedYes
Event34th International Conference on Machine Learning, ICML 2017 - Sydney, Australia
Duration: 6 Aug 201711 Aug 2017

Publication series

Name34th International Conference on Machine Learning, ICML 2017
Volume6

Conference

Conference34th International Conference on Machine Learning, ICML 2017
Country/TerritoryAustralia
CitySydney
Period6/08/1711/08/17

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software

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