A stochastic version of stein variational gradient descent for efficient sampling

Published

Journal Article

© Mathematical Sciences Publishers. We propose in this work RBM-SVGD, a stochastic version of the Stein variational gradient descent (SVGD) method for efficiently sampling from a given probability measure, which is thus useful for Bayesian inference. The method is to apply the random batch method (RBM) for interacting particle systems proposed by Jin et al. to the interacting particle systems in SVGD. While keeping the behaviors of SVGD, it reduces the computational cost, especially when the interacting kernel has long range. We prove that the one marginal distribution of the particles generated by this method converges to the one marginal of the interacting particle systems under Wasserstein-2 distance on fixed time interval T0; T U. Numerical examples verify the efficiency of this new version of SVGD.

Full Text

Duke Authors

Cited Authors

  • Li, L; Li, Y; Liu, JG; Liu, Z; Lu, J

Published Date

  • January 1, 2020

Published In

Volume / Issue

  • 15 / 1

Start / End Page

  • 37 - 63

Electronic International Standard Serial Number (EISSN)

  • 2157-5452

International Standard Serial Number (ISSN)

  • 1559-3940

Digital Object Identifier (DOI)

  • 10.2140/camcos.2020.15.37

Citation Source

  • Scopus