  Stein Variational Gradient Descent (SVGD) is a popular particle-based method
for Bayesian inference. However, its convergence suffers from the variance
collapse, which reduces the accuracy and diversity of the estimation. In this
paper, we study the isotropy property of finite particles during the
convergence process and show that SVGD of finite particles cannot spread across
the entire sample space. Instead, all particles tend to cluster around the
particle center within a certain range and we provide an analytical bound for
this cluster. To further improve the effectiveness of SVGD for high-dimensional
problems, we propose the Augmented Message Passing SVGD (AUMP-SVGD) method,
which is a two-stage optimization procedure that does not require sparsity of
the target distribution, unlike the MP-SVGD method. Our algorithm achieves
satisfactory accuracy and overcomes the variance collapse problem in various
benchmark problems.
