The empirical measure with samples from some probability measure (which might be known up to a multiplicative factor) has many applications in Bayesian inference [1, 2] and data assimilation [3]. A class of widely used sampling methods is the Markov Chain Monte Carlo (MCMC) methods, where the trajectory of a particle is given by some constructed Markov chain with the desired distribution invariant. The trajectory of the particle is clearly stochastic, and the Monte Carlo methods take effect slowly for small number of samples. Unlike MCMC, the Stein variational Gradient method (proposed by Liu and Wang in [4]) belongs to particle based variational inference sampling methods (see also [5, 6]). These methods update particles by solving optimization problems, and each iteration is expected to make progress. As a nonparametric variational inference method, SVGD gives a deterministic way to generate points that approximate the desired probability distribution by solving an ODE system.