# A stochastic version of Stein Variational Gradient Descent for efficient sampling

## TL;DR

This paper introduces RBM-SVGD, a stochastic variant of Stein Variational Gradient Descent that leverages the Random Batch Method to reduce computational costs in sampling tasks, especially with long-range kernels.

## Contribution

The paper develops RBM-SVGD, integrating RBM into SVGD to improve efficiency for Bayesian sampling without sacrificing core behaviors.

## Key findings

- RBM-SVGD reduces computational cost compared to standard SVGD.
- Numerical experiments demonstrate the efficiency of RBM-SVGD.
- RBM-SVGD maintains the effectiveness of SVGD in sampling tasks.

## Abstract

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.