A stochastic version of Stein Variational Gradient Descent for efficient sampling

Understand

We propose in this work RBM-SVGD, a stochastic version of Stein Variational Gradient Descent (SVGD) method for efficiently sampling from a given probability measure and 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.
• Numerical examples verify the efficiency of this new version of SVGD.