A stochastic version of Stein Variational Gradient Descent for efficient sampling
This work addresses computational bottlenecks in Bayesian inference methods for researchers and practitioners, though it is incremental as it builds on existing SVGD and Random Batch Method techniques.
The authors tackled the computational inefficiency of Stein Variational Gradient Descent (SVGD) for sampling in Bayesian inference by proposing RBM-SVGD, a stochastic version that reduces computational cost while maintaining performance, as verified through numerical examples.
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.