Relative Entropy Gradient Sampler for Unnormalized Distributions

Understand

We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions.

• REGS is a particle method that seeks a sequence of simple nonlinear transforms iteratively pushing the initial samples from a reference distribution into the samples from an unnormalized target distribution.
• To determine the nonlinear transforms at each iteration, we consider the Wasserstein gradient flow of relative entropy.
• This gradient flow determines a path of probability distributions that interpolates the reference distribution and the target distribution.